2024 ESIF Economics and AI+ML Meeting

August 13, 2024
Time	Location	Event
08:00 to 08:45	Park Atrium	Breakfast
08:45 to 10:20	Alice Statler Auditorium	Keynote lectures
10:20 to 10:50	Park Atrium	Break
10:50 to 11:50	see below	Contributed Parallel Sessions – Time Block I
11:50 to 12:00	Park Atrium	Intermission
12:00 to 13:00	see below	Contributed Parallel Sessions – Time Block II
13:00 to 14:30	Statler Hotel Ballroom	Lunch
14:30 to 15:50	see below	Contributed Parallel Sessions – Time Block III
15:50 to 16:20	Park Atrium	Break
16:20 to 17:40	see below	Contributed Parallel Sessions – Time Block IV
18:00 to 20:00	Statler Hotel Ballroom	Reception
August 14, 2024
Time	Location	Event
08:00 to 08:45	Park Atrium	Breakfast
08:50 to 10:20	Alice Statler Auditorium	Keynote lectures
10:20 to 10:50	Park Atrium	Break
10:50 to 11:50	see below	Contributed Parallel Sessions – Time Block V
11:50 to 12:00	Park Atrium	Intermission
12:00 to 13:00	see below	Contributed Parallel Sessions – Time Block VI
13:00 to 14:30	Statler Hotel Ballroom	Lunch
14:30 to 15:50	see below	Contributed Parallel Sessions – Time Block VII
15:50 to 16:20	Park Atrium	Break
16:20 to 17:50	Alice Statler Auditorium	Keynote lectures
18:00 to 20:00	Statler Hotel Ballroom	Dinner

Summary of All Sessions
Click here for an index of all participants

#	Date/Time	Type	Title/Location	Papers
1	August 13, 2024 8:45-10:20	invited	Keynote Lecture 1     Location: Alice Statler Auditorium	2
2	August 13, 2024 10:50-11:50	contributed	Automation and Firm Productivity     Location: Parallel 5, Room 291	3
3	August 13, 2024 10:50-11:50	contributed	Human-AI interaction (1)     Location: Parallel 1, Room 196	3
4	August 13, 2024 10:50-11:50	contributed	Missing Data Reweighting and Inference     Location: Parallel 6, Room 391	3
5	August 13, 2024 10:50-11:50	contributed	Network Effects     Location: Parallel 4, Room 265	4
6	August 13, 2024 10:50-11:50	contributed	No-regret Algorithms and Resulting Outcomes (1)     Location: Parallel 2, Room 165	3
7	August 13, 2024 10:50-11:50	contributed	Text and Speech Analysis in Macro     Location: Parallel 3, Room 198	3
8	August 13, 2024 12:00-13:00	contributed	Causal Inference with Interference     Location: Parallel 4, Room 265	3
9	August 13, 2024 12:00-13:00	contributed	Computational advances in solving General Equilibrium models     Location: Parallel 6, Room 391	3
10	August 13, 2024 12:00-13:00	contributed	Human-AI interaction (2)     Location: Parallel 1, Room 196	3
11	August 13, 2024 12:00-13:00	contributed	Inference for Panel Data     Location: Parallel 5, Room 291	2
12	August 13, 2024 12:00-13:00	contributed	No-regret Algorithms and Resulting Outcomes (2)     Location: Parallel 2, Room 165	3
13	August 13, 2024 12:00-13:00	contributed	Persuasion and Information Design (1)     Location: Parallel 3, Room 198	3
14	August 13, 2024 14:30-15:50	contributed	Doubly Robust Methods     Location: Parallel 4, Room 265	4
15	August 13, 2024 14:30-15:50	contributed	Evaluation of Predictive Algorithms     Location: Parallel 1, Room 196	4
16	August 13, 2024 14:30-15:50	contributed	Inference with and without Sparsity     Location: Parallel 2, Room 165	4
17	August 13, 2024 14:30-15:50	contributed	ML in finance and asset pricing     Location: Parallel 5, Room 291	3
18	August 13, 2024 14:30-15:50	contributed	Persuasion and Information Design (2)     Location: Parallel 3, Room 198	4
19	August 13, 2024 14:30-15:50	contributed	Privacy and Price Discrimination     Location: Parallel 6, Room 391	4
20	August 13, 2024 16:20-17:40	contributed	Analysis of non-standard data (1)     Location: Parallel 2, Room 165	4
21	August 13, 2024 16:20-17:40	contributed	Estimation of Treatment Effects     Location: Parallel 1, Room 196	4
22	August 13, 2024 16:20-17:40	contributed	High dimensional Regression Methods     Location: Parallel 6, Room 391	4
23	August 13, 2024 16:20-17:40	contributed	Mechanism Design     Location: Parallel 4, Room 265	4
24	August 13, 2024 16:20-17:40	contributed	ML for Forecasting and Risk Evaluation     Location: Parallel 5, Room 291	3
25	August 13, 2024 16:20-17:40	contributed	Statistical Inference with Sequential Experiments     Location: Parallel 3, Room 198	4
26	August 14, 2024 8:50-10:20	invited	Keynote lecture 2     Location: Alice Statler Auditorium	2
27	August 14, 2024 10:50-11:50	contributed	Admission Evaluation     Location: Parallel 2, Room 165	3
28	August 14, 2024 10:50-11:50	contributed	Advances in the Use and Theory of LLM (1)     Location: Parallel 4, Room 265	3
29	August 14, 2024 10:50-11:50	contributed	Human-AI interaction (3)     Location: Parallel 1, Room 196	3
30	August 14, 2024 10:50-11:50	contributed	No-regret Algorithms and Resulting Outcomes (3)     Location: Parallel 3, Room 198	3
31	August 14, 2024 10:50-11:50	contributed	Online Learning and Recommender Systems     Location: Parallel 6, Room 391	3
32	August 14, 2024 12:00-13:00	contributed	Advances in the Use and Theory of LLM (2)     Location: Parallel 1, Room 196	3
33	August 14, 2024 12:00-13:00	contributed	Algorithmic Collusion     Location: Parallel 4, Room 265	3
34	August 14, 2024 12:00-13:00	contributed	Algorithmic Decision Making and Statistical Inference     Location: Parallel 5, Room 291	3
35	August 14, 2024 12:00-13:00	contributed	Learning in Stackelberg Game Environment     Location: Parallel 3, Room 198	3
36	August 14, 2024 12:00-13:00	contributed	Optimal Treatment Choice     Location: Parallel 2, Room 165	3
37	August 14, 2024 12:00-13:00	contributed	Pricing using Reinforcement Learning     Location: Parallel 6, Room 391	3
38	August 14, 2024 14:30-15:50	contributed	Analysis of Online Posts     Location: Parallel 3, Room 198	4
39	August 14, 2024 14:30-15:50	contributed	AI and the future of work     Location: Parallel 5, Room 291	4
40	August 14, 2024 14:30-15:50	contributed	Algorithmic Decision Making and Human-AI Interaction     Location: Parallel 1, Room 196	4
41	August 14, 2024 14:30-15:50	contributed	Analysis of non-standard data (2)     Location: Parallel 4, Room 265	4
42	August 14, 2024 14:30-15:50	contributed	Pricing in Markets     Location: Parallel 6, Room 391	4
43	August 14, 2024 14:30-15:50	contributed	Statistical Decisions and Experiments     Location: Parallel 2, Room 165	4
44	August 14, 2024 16:20-17:50	invited	Keynote lecture 3     Location: Alice Statler Auditorium	2

 

44 sessions, 145 papers, and 0 presentations with no associated papers

2024 ESIF Economics and AI+ML Meeting
Detailed List of Sessions

                                                                                        

Session 1: Keynote Lecture 1 August 13, 2024 8:45 to 10:20 Location: Alice Statler Auditorium
Session Chair: Francesca Molinari, Cornell
Session type: invited
Efficiently Learning Personalized Policies
By Emma Brunskill; Stanford University
presented by: Emma Brunskill, Stanford University
Conditional Influence Functions for Nonparametric Parameters
By Whitney Newey; Massachusetts Institute of Technology
presented by: Whitney Newey, Massachusetts Institute of Technology
Session 2: Automation and Firm Productivity August 13, 2024 10:50 to 11:50 Location: Parallel 5, Room 291
Session Chair: Iulia Siedschlag, Economic and Social Research Institute Dublin
Session type: contributed
Automation and the Rise of Superstar Firms
By Hamid Firooz; University of Rochester Zheng Liu; Federal Reserve Bank of San Francisco YAJIE WANG; University of Missouri
presented by: Hamid Firooz, University of Rochester
Connected by Data: Evidence from Job Postings in China
[slides]
By Yao-Yu Chih; Texas State University Zexuan Liu; Nanjing Audit University
presented by: Yao-Yu Chih, Texas State University
Artificial Intelligence and Firm Productivity
By Dr Siedschlag; Economic and Social Research Institute Dublin Juan Duran; Economic and Social Research Institute
presented by: Iulia Siedschlag, Economic and Social Research Institute Dublin
Session 3: Human-AI interaction (1) August 13, 2024 10:50 to 11:50 Location: Parallel 1, Room 196
Session Chair: Keaton Ellis,
Session type: contributed
The Value of Context: Human versus Black Box Evaluators
By Andrei Iakovlev; Northwestern Annie Liang; Northwestern University
presented by: Andrei Iakovlev, Northwestern University
Endogenous Information Acquisition in Cheap-Talk Games
By
presented by: Sophie Kreutzkamp, University of Oxford
The Predictivity of Theories of Choice Under Uncertainty
By Keaton Ellis Shachar Kariv; University of California, Berkeley Erkut Ozbay; University of Maryland
presented by: Keaton Ellis,
Session 4: Missing Data Reweighting and Inference August 13, 2024 10:50 to 11:50 Location: Parallel 6, Room 391
Session Chair: Jinglin Wang, New York University
Session type: contributed
Statistical inference for generative adversarial networks and other minimax problems
By
presented by: Mika Meitz, University of Helsinki
Exploiting observation bias to improve matrix completion
By Yassir Jedra; MIT Sean Mann; MIT Charlotte Park; Massachusetts Institute of Technology Devavrat Shah; MIT
presented by: Yassir Jedra, MIT
Optimal Survey Weights
[slides]
By Elena Manresa; NYU Jinglin Wang; New York University
presented by: Jinglin Wang, New York University
Session 5: Network Effects August 13, 2024 10:50 to 11:50 Location: Parallel 4, Room 265
Session Chair: John Lazarev, NYU
Session type: contributed
A Strategic Model of Software Dependency Networks
By Cornelius Fritz; Penn State University Co-Pierre Georg; University of Cape Town Angelo Mele; Johns Hopkins University Michael Schweinberger; Penn State University
presented by: Angelo Mele, Johns Hopkins University
Generative AI and User-Generated Content: Evidence from Online Reviews
By Samsun Knight; University of Toronto Yakov Bart; Northeastern University
presented by: Samsun Knight, University of Toronto
Social Media and Job Market Success: A Field Experiment on Twitter
By Yan Chen; University of Michigan Alain Cohn; University of Michigan Jingyi Qiu; University of Michigan Alvin Roth; Stanford University
presented by: Jingyi Qiu, University of Michigan
Quantifying Delay Propagation in Airline Networks
[slides]
By Liyu Dou; Singapore Management University Jakub Kastl; Princeton John Lazarev; NYU
presented by: John Lazarev, NYU
Session 6: No-regret Algorithms and Resulting Outcomes (1) August 13, 2024 10:50 to 11:50 Location: Parallel 2, Room 165
Session Chair: Meena Jagadeesan, UC Berkeley
Session type: contributed
Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability
By Natalie Collina; University of Pennsylvania Varun Gupta; University of Pennsylvania Aaron Roth; Penn
presented by: Natalie Collina, University of Pennsylvania
Forecasting for Swap Regret for All Downstream Agents
By Aaron Roth; Penn Mirah Shi; University of Pennsylvania
presented by: Mirah Shi, University of Pennsylvania
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
By Meena Jagadeesan; UC Berkeley Michael Jordan; University of California, Berkeley Jacob Steinhardt; UC Berkeley Nika Haghtalab; UC Berkeley
presented by: Meena Jagadeesan, UC Berkeley
Session 7: Text and Speech Analysis in Macro August 13, 2024 10:50 to 11:50 Location: Parallel 3, Room 198
Session Chair: Larissa Schwaller, University of Bern
Session type: contributed
Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements
By Taeyoung Doh; Federal Reserve Bank of Kansas City Dongho Song; Johns Hopkins University
presented by: Taeyoung Doh, Federal Reserve Bank of Kansas City
Emotion in Euro Area Monetary Policy Communication and Bond Yields: The Draghi Era
By Dimitrios Kanelis; University of Muenster Pierre Siklos; Wilfrid Laurier University
presented by: Pierre Siklos, Wilfrid Laurier University
Using Natural Language Processing to Identify Monetary Policy Shocks
By Alexandra Piller; Study Center Gerzensee Marc Schranz; University of Bern Larissa Schwaller; University of Bern
presented by: Larissa Schwaller, University of Bern
Session 8: Causal Inference with Interference August 13, 2024 12:00 to 13:00 Location: Parallel 4, Room 265
Session Chair: Eric Auerbach, Northwestern University
Session type: contributed
Combining Rollout Designs and Clustering for Causal Inference under Low-order Interference
By Mayleen Cortez-Rodriguez; Cornell University Matthew Eichhorn; Cornell University Christina Yu; Cornell University
presented by: Mayleen Cortez-Rodriguez, Cornell University
Causal clustering: design of cluster experiments under network interference
By Davide Viviano; Harvard Lihua Lei; Stanford University Guido Imbens; Stanford University Brian Karrer; Meta Okke Schrijvers; Meta Inc Liang Shi; Meta Inc
presented by: Lihua Lei, Stanford University
Identifying Socially Disruptive Policies
By Eric Auerbach; Northwestern University Yong Cai; University of Chicago
presented by: Eric Auerbach, Northwestern University
Session 9: Computational advances in solving General Equilibrium models August 13, 2024 12:00 to 13:00 Location: Parallel 6, Room 391
Session Chair: Yaolang Zhong, University of Warwick
Session type: contributed
Intergenerational Consequences of Rare Disasters
By Marlon Azinovic; University of Pennsylvania Jan Žemlička; University of Zürich, Department of Banking and Finance
presented by: Marlon Azinovic, University of Pennsylvania
Deep Learning for Search and Matching Models
By Jonathan Payne; Princeton University Adam Rebei; Stanford University Yucheng Yang; University of Zurich
presented by: Jonathan Payne, Princeton University
Operator Learning in Macroeconomics
By
presented by: Yaolang Zhong, University of Warwick
Session 10: Human-AI interaction (2) August 13, 2024 12:00 to 13:00 Location: Parallel 1, Room 196
Session Chair: Adam Harris, Massachusetts Institute of Technology
Session type: contributed
Should Humans Lie to Machines? The Incentive Compatibility of Lasso and GLM Structured Sparsity Estimators
By
presented by: Mehmet Caner, North Carolina University
AI Oversight and Human Mistakes: Evidence from Centre Court
By David Almog; Kellogg School of Management, Northwestern University Romain Gauriot; Deakin University Lionel Page; University of Queensland Daniel Martin; University of California, Santa Barbara
presented by: Daniel Martin, University of California, Santa Barbara
Decision-making with machine prediction: Evidence from predictive maintenance in trucking
By
presented by: Adam Harris, Massachusetts Institute of Technology
Session 11: Inference for Panel Data August 13, 2024 12:00 to 13:00 Location: Parallel 5, Room 291
Session Chair: Konrad Menzel, New York University
Session type: contributed
Estimating Latent-Variable Panel Data Models Using Parameter-Expanded SEM Methods
By
presented by: SIQI WEI, IE University
Structural Sieves
By
presented by: Konrad Menzel, New York University
Session 12: No-regret Algorithms and Resulting Outcomes (2) August 13, 2024 12:00 to 13:00 Location: Parallel 2, Room 165
Session Chair: Lorenzo Magnolfi, University of Wisconsin-Madison
Session type: contributed
Liquid Welfare Guarantees for No-Regret Learning in Sequential Budgeted Auctions
By Giannis Fikioris; Cornell University Eva Tardos; Cornell
presented by: Giannis Fikioris, Cornell University
Auctions between Regret-Minimizing Agents
By Yoav Kolumbus; Cornell Noam Nisan; Hebrew University of Jerusalem
presented by: Yoav Kolumbus, Cornell
Estimation of Games under No Regret: Structural Econometrics for AI
By Niccolò Lomys; CSEF & Università degli Studi di Napoli Federico II Lorenzo Magnolfi; University of Wisconsin-Madison
presented by: Lorenzo Magnolfi, University of Wisconsin-Madison
Session 13: Persuasion and Information Design (1) August 13, 2024 12:00 to 13:00 Location: Parallel 3, Room 198
Session Chair: Safwan Hossain, Harvard University
Session type: contributed
Persuasion, Delegation, and Private Information in Algorithm-Assisted Decisions
By
presented by: Ruqing Xu, Cornell University
Persuading a Learning Agent
By Tao Lin; Harvard University Yiling Chen; Harvard University
presented by: Tao Lin, Harvard University
Multi-Sender Persuasion - A Computational Perspective
By Safwan Hossain; Harvard University Tonghan Wang; Harvard Tao Lin; Harvard University Yiling Chen; Harvard University David Parkes; Harvard University Haifeng Xu; University of Chicago
presented by: Safwan Hossain, Harvard University
Session 14: Doubly Robust Methods August 13, 2024 14:30 to 15:50 Location: Parallel 4, Room 265
Session Chair: Rahul Singh, Harvard University
Session type: contributed
Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions
By Gyungbae Park; Brown University
presented by: Gyungbae Park, Brown University
Within-&Across-Cluster Dependence Robust Double/Debiased Machine Learning for Panel Models
[slides]
By
presented by: Kaicheng Chen, Michigan State University
Data-Driven Influence Functions for Optimization-Based Causal Inference
By Michael Jordan; University of California, Berkeley Angela Zhou
presented by: Angela Zhou,
Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals
By Victor Chernozhukov; Massachusetts Institute of Technology Whitney Newey; Massachusetts Institute of Technology Rahul Singh; Harvard University Vasilis Syrgkanis; Microsoft Research
presented by: Rahul Singh, Harvard University
Session 15: Evaluation of Predictive Algorithms August 13, 2024 14:30 to 15:50 Location: Parallel 1, Room 196
Session Chair: Jason Hartline, Northwestern University
Session type: contributed
Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
By Ashesh Rambachan Amanda Coston; Carnegie Mellon University Edward Kennedy; Carnegie Mellon University
presented by: Ashesh Rambachan,
Domain constraints improve risk prediction when outcome data is missing
By Sidhika Balachandar; Cornell University Nikhil Garg; Cornell Emma Pierson; Cornell
presented by: Sidhika Balachandar, Cornell University
Predictive Enforcement
[slides]
By Yeon-Koo Che; Columbia University Jinwoo Kim; Seoul National University
presented by: Yeon-Koo Che, Columbia University
Bias-variance Games
By Yiding Feng Ronen Gradwohl; Ariel University Jason Hartline; Northwestern University Aleck Johnsen; unaffiliated Denis Nekipelov; Department of Economics
presented by: Jason Hartline, Northwestern University
Session 16: Inference with and without Sparsity August 13, 2024 14:30 to 15:50 Location: Parallel 2, Room 165
Session Chair: José Luis Montiel Olea, Cornell University
Session type: contributed
The Fragility of Sparsity
By Michal Kolesar; Princeton University Ulrich Mueller; Princeton University Sebastian Roelsgaard; Princeton University
presented by: Sebastian Roelsgaard, Princeton University
Can Machines Learn Weak Signals?
By Zhouyu Shen; University of Chicago Dacheng Xiu; University of Chicago
presented by: Dacheng Xiu, University of Chicago
Inference for Large Panel Data with Many Covariates
By Markus Pelger; Stanford University Jiacheng Zou; Stanford University
presented by: Jiacheng Zou, Stanford University
The out-of-sample prediction error of the $\sqrt{\text{LASSO}}$ and related estimators
By José Luis Montiel Olea; Cornell University
presented by: José Luis Montiel Olea, Cornell University
Session 17: ML in finance and asset pricing August 13, 2024 14:30 to 15:50 Location: Parallel 5, Room 291
Session Chair: David Rapach, Federal Reserve Bank of Atlanta
Session type: contributed
Sparse Modeling Under Grouped Heterogeneity with an Application to Asset Pricing
By Lin William Cong; Cornell University Guanhao Feng; City University of Hong Kong Jingyu He; City University of Hong Kong Junye Li; Fudan University
presented by: Lin William Cong, Cornell University
Variable selection for minimum-variance portfolios
By Guilherme Moura; UFSC Andre Santos; CUNEF Universidad Hudson Torrent; UFRGS
presented by: Andre Santos, CUNEF Universidad
Cryptocurrency Return Predictability: A Machine-Learning Analysis
By Ilias Filippou; Washington University in St. Louis David Rapach; Federal Reserve Bank of Atlanta Christoffer Thimsen; Aarhus University
presented by: David Rapach, Federal Reserve Bank of Atlanta
Session 18: Persuasion and Information Design (2) August 13, 2024 14:30 to 15:50 Location: Parallel 3, Room 198
Session Chair: Ce Li, Boston University
Session type: contributed
Algorithmic Choice Architecture for Boundedly Rational Consumers
By Stefan Bucher; New York University Peter Dayan; University College London
presented by: Stefan Bucher, New York University
Platforms for Efficient and Incentive-Aware Collaboration
By Nika Haghtalab; UC Berkeley Mingda Qiao; UC Berkeley Kunhe Yang; UC Berkeley
presented by: Kunhe Yang, UC Berkeley
Steering No-Regret Learners to a Desired Equilibrium
By Brian Zhang; Carnegie Mellon University Gabriele Farina; MIT Ioannis Anagnostides; Carnegie Mellon University Frederico Cacciamani; Politecnico di Milano Stephen Mcaleer; Carnegie Mellon University Andreas Haupt; MIT Andrea Celli; University of Bocconi Nicola Gatti; Politecnico di Milano Vincent Conitzer; Duke University Tuomas Sandholm; Carnegie Mellon University
presented by: Brian Zhang, Carnegie Mellon University
Information Design Without Prior or State
By Ce Li; Boston University Tao Lin; Harvard University
presented by: Ce Li, Boston University
Session 19: Privacy and Price Discrimination August 13, 2024 14:30 to 15:50 Location: Parallel 6, Room 391
Session Chair: Guy Aridor, Northwestern university
Session type: contributed
Consumer Profiling via Information Design
By Itay Fainmesser; The Johns Hopkins University Andrea Galeotti; LBS Ruslan Momot; Ross School of Business, University of M
presented by: Itay Fainmesser, The Johns Hopkins University
Privacy and Polarization: An Inference-Based Framework
By Tommaso Bondi Omid Rafieian; Cornell University
presented by: Tommaso Bondi,
The Limits of Price Discrimination Under Privacy Constraints
By Alireza Fallah; University of California, Berkeley Michael Jordan; University of California, Berkeley Ali Makhdoumi; Duke University Azarakhsh Malekian; University of Toronto
presented by: Alireza Fallah, University of California, Berkeley
Privacy Regulation and Targeted Advertising: Evidence from Apple’s App Tracking Transparency
By Guy Aridor; Northwestern university Yeon-Koo Che; Columbia University Brett Hollenbeck; UCLA Anderson Maximilian Kaiser; Grips Intelligence Daniel McCarthy; Emory University
presented by: Guy Aridor, Northwestern university
Session 20: Analysis of non-standard data (1) August 13, 2024 16:20 to 17:40 Location: Parallel 2, Room 165
Session Chair: Xi Chen, Yale University and IZA
Session type: contributed
From Predictive Algorithms to Automatic Generation of Anomalies
By Sendhil Mullainathan; University of Chicago Ashesh Rambachan
presented by: Ashesh Rambachan,
An experimental approach to measure social bias in vision-language models
By Carina Hausladen; ETHZ Manuel Knott; ETHZ Pietro Perona; Caltech Colin Camerer; California Institute of Technology
presented by: Carina Hausladen, ETHZ
Leveraging AI to Uncover Early Sources of Inequality: Evidence from Two Field Experiments
By
presented by: Julie Pernaudet, University of Chicago
Childhood Circumstances and Health of American and Chinese Older Adults: A Machine Learning Evaluation of Inequality of Opportunity in Health
By Shutong Huo; University of California, Irvine Xi Chen; Yale University and IZA
presented by: Xi Chen, Yale University and IZA
Session 21: Estimation of Treatment Effects August 13, 2024 16:20 to 17:40 Location: Parallel 1, Room 196
Session Chair: Justin Whitehouse, Carnegie Mellon University
Session type: contributed
Robust inference for the treatment effect variance in experiments using machine learning
By
presented by: Alejandro Sanchez Becerra, Emory University
Doubly Robust Inference in Causal Latent Factor Models
By Alberto Abadie; MIT Anish Agarwal; Columbia University Raaz Dwivedi; Cornell Tech Abhin Shah; MIT
presented by: Raaz Dwivedi, Cornell Tech
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
By JIKAI JIN; Stanford University Vasilis Syrgkanis; Microsoft Research
presented by: JIKAI JIN, Stanford University
Orthogonal Calibration of Causal Estimators
By Christopher Jung; Stanford University Vasilis Syrgkanis; Microsoft Research Justin Whitehouse; Carnegie Mellon University Bryan Wilder; Carnegie Mellon University Zhiwei Wu; Carnegie Mellon University
presented by: Justin Whitehouse, Carnegie Mellon University
Session 22: High dimensional Regression Methods August 13, 2024 16:20 to 17:40 Location: Parallel 6, Room 391
Session Chair: Weijie Su, University of Pennsylvania
Session type: contributed
Selecting Penalty Parameters of High-Dimensional M-Estimators using Bootstrapping after Cross-Validation
[slides]
By Denis Chetverikov; University of California Los Angeles Jesper Sørensen; University of Copenhagen
presented by: Jesper Sørensen, University of Copenhagen
Free Discontinuity Regression: With an Application to the Economic Effects of Internet Shutdowns
By Florian Gunsilius; Univeristy of Michigan David Van Dijcke; University of Michigan
presented by: David Van Dijcke, University of Michigan
Functional Partial Least-Squares: Optimal Rayes and Adaptation
By Andrii Babii; UNC-Chapel Hill Marine Carrasco; University of Montreal
presented by: Marine Carrasco, University of Montreal
DEEP PARTIALLY LINEAR MODELS
By Zhiqi Bu; Amazon Web Services AI Yufan Chen; Peking University Weijie Su; University of Pennsylvania Lintong Wu; Peking University Ruixun Zhang; Peking University
presented by: Weijie Su, University of Pennsylvania
Session 23: Mechanism Design August 13, 2024 16:20 to 17:40 Location: Parallel 4, Room 265
Session Chair: Yanchen Jiang, Harvard University
Session type: contributed
Contracting with a Learning Agent
By Guru Guruganesh; Google Research Yoav Kolumbus; Cornell Jon Schneider; Google Research Inbal Talgam-Cohen; Tel Aviv University Emmanouil-Vasileios Vlatakis-Gkaragkouni; Berkeley Joshua Wang; Google Research Matt Weinberg; MIT
presented by: Jon Schneider,
Bicriteria Multidimensional Mechanism Design with Side Information
By Nina Balcan; Carnegie Mellon University Siddharth Prasad; Carnegie Mellon University Tuomas Sandholm; Carnegie Mellon University
presented by: Siddharth Prasad, Carnegie Mellon University
Mechanism Design for Large Language Models
By Paul Duetting; Google Research Vahab MIrrokni; Google Research Renato Paes Leme; Google Research Haifeng Xu; University of Virginia Song Zuo; Google Research
presented by: Renato Paes Leme, Google Research
Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning
By Tonghan Wang; Harvard Yanchen Jiang; Harvard University David Parkes; Harvard University
presented by: Yanchen Jiang, Harvard University
Session 24: ML for Forecasting and Risk Evaluation August 13, 2024 16:20 to 17:40 Location: Parallel 5, Room 291
Session Chair: Tengjia Shu, University of Illinois Chicago
Session type: contributed
Forecast Combination and Interpretability Using Random Subspace
By
presented by: Boris Kozyrev, Halle Institute for Economic Research (IWH)
Bagged Pretested Forecast Combination
By Ekaterina Kazak; University of Manchester Roxana Halbleib; University of Freiburg Winfried Pohlmeier; University of Konstanz
presented by: Winfried Pohlmeier, University of Konstanz
Evaluating Hedge Funds with Machine Learning-Based Benchmarks
By Tengjia Shu; University of Illinois Chicago Ashish Tiwari; University of Iowa
presented by: Tengjia Shu, University of Illinois Chicago
Session 25: Statistical Inference with Sequential Experiments August 13, 2024 16:20 to 17:40 Location: Parallel 3, Room 198
Session Chair: Bo Zhou, Virginia Tech
Session type: contributed
Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits
By Keisuke Hirano; Pennsylvania State University Jack Porter; University of Wisconsin
presented by: Keisuke Hirano, Pennsylvania State University
Post Reinforcement Learning Inference
By Vasilis Syrgkanis; Microsoft Research Ruohan Zhan; Hong Kong University of Science and Technology
presented by: Ruohan Zhan, Hong Kong University of Science and Technology
Multiagent Apprenticeship and Inverse Reinforcement Learning
By Denizalp Goktas; Brown University Sadie Zhao; Harvard University Amy Greenwald; Brown University
presented by: Sadie Zhao, Harvard University
Bandit Limit Experiment
By Ramon van den Akker; Tilburg University Bas Werker; Tilburg University Bo Zhou; Virginia Tech
presented by: Bo Zhou, Virginia Tech
Session 26: Keynote lecture 2 August 14, 2024 8:50 to 10:20 Location: Alice Statler Auditorium
Session Chair: David Shmoys, Cornell University
Session type: invited
On learning in the presence of biased data and strategic behavior
By Avrim Blum; Toyota Technological Institute at Chicago
presented by: Avrim Blum, Toyota Technological Institute at Chicago
Taming the Curse of Dimensionality: Old Ideas and New Strategies
By Jesus Fernandez-Villaverde; University of Pennsylvania
presented by: Jesus Fernandez-Villaverde, University of Pennsylvania
Session 27: Admission Evaluation August 14, 2024 10:50 to 11:50 Location: Parallel 2, Room 165
Session Chair: S. Nageeb Ali, Pennsylvania State University
Session type: contributed
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
By Susan Athey; Stanford University Niall Keleher; Innovations for Poverty Action Jann Spiess; Stanford University
presented by: Jann Spiess, Stanford University
Monoculture in Matching Markets
By Kenny Peng; Cornell Tech Nikhil Garg; Cornell
presented by: Kenny Peng, Cornell Tech
Common Versus Independent Standards
By S. Nageeb Ali; Pennsylvania State University Salvador Candelas; Pennsylvania State University Ran Shorrer; Penn State
presented by: S. Nageeb Ali, Pennsylvania State University
Session 28: Advances in the Use and Theory of LLM (1) August 14, 2024 10:50 to 11:50 Location: Parallel 4, Room 265
Session Chair: Simon Freyaldenhoven, Federal Reserve Bank of Philadelphia
Session type: contributed
Automated Social Science: Language Models as Scientist and Subjects
By Benjamin Manning; MIT Kehang Zhu; Harvard John Horton; MIT & NBER
presented by: Benjamin Manning, MIT
Identification and Estimation of Multinomial Logit Models with Finite Mixtures
By
presented by: Dingyi Li, Cornell University
On the Testability of the Anchor-Words Assumption in Topic Models
By Simon Freyaldenhoven; Federal Reserve Bank of Philadelphia Barry Ke; Department of Applied Mathematics Dingyi Li; Cornell University Jose Luis Montiel Olea; Cornell University
presented by: Simon Freyaldenhoven, Federal Reserve Bank of Philadelphia
Session 29: Human-AI interaction (3) August 14, 2024 10:50 to 11:50 Location: Parallel 1, Room 196
Session Chair: Keer Yang, University of California, Davis
Session type: contributed
Modeling Machine Learning: A Cognitive Economic Approach
By Andrew Caplin; New York University Daniel Martin; University of California, Santa Barbara Philip Marx; Louisiana State University
presented by: Daniel Martin, University of California, Santa Barbara
Distinguishing the Indistinguishable: Human Expertise in Algorithmic Prediction
[slides]
By Rohan Alur; Massachusetts Institute of Technology Manish Raghavan; Massachusetts Institute of Technology Devavrat Shah; MIT
presented by: Rohan Alur, Massachusetts Institute of Technology
Behavioral Machine Learning? Computer Predictions of Corporate Earnings also Overreact
By Murray Frank; University of Minnesota Jing Gao; University of Minnesota Keer Yang; University of California, Davis
presented by: Keer Yang, University of California, Davis
Session 30: No-regret Algorithms and Resulting Outcomes (3) August 14, 2024 10:50 to 11:50 Location: Parallel 3, Room 198
Session Chair: Natalie Collina, University of Pennsylvania
Session type: contributed
Polynomial-Time Linear-Swap Regret Minimization in Imperfect-Information Sequential Games
By Gabriele Farina; MIT Charilaos Pipis; MIT
presented by: Gabriele Farina, MIT
Rethinking Scaling Laws for Learning in Strategic Environments
By Tinashe Handina; California Institute of Technology Eric Mazumdar; California Institute of Technology
presented by: Tinashe Handina, California Institute of Technology
Pareto-Optimal Algorithms for Learning in Games
By Eshwar Ram Arunachaleswaran; University of Pennsylvania Natalie Collina; University of Pennsylvania Jon Schneider
presented by: Natalie Collina, University of Pennsylvania
Session 31: Online Learning and Recommender Systems August 14, 2024 10:50 to 11:50 Location: Parallel 6, Room 391
Session Chair: Hongseok Namkoong, Columbia University
Session type: contributed
The SMART Approach to Instance-Optimal Online Learning
By Siddhartha Banerjee; Cornell University Alankrita Bhatt; California Institute of Technology Christina Yu; Cornell University
presented by: Christina Yu, Cornell University
Incentivized Exploration via Filtered Posterior Sampling
By Anand Kalvit; Stanford University Aleksandrs Slivkins; Microsoft Research Yonatan Gur; Stanford University
presented by: Anand Kalvit, Stanford University
Posterior Sampling via Autoregressive Generation
By Kelly Zhang Tiffany Cai; Columbia University Hongseok Namkoong; Columbia University Daniel Russo
presented by: Hongseok Namkoong, Columbia University
Session 32: Advances in the Use and Theory of LLM (2) August 14, 2024 12:00 to 13:00 Location: Parallel 1, Room 196
Session Chair: Keyon Vafa, Harvard University
Session type: contributed
Value Aligned Large Language Models
By Panagiotis Angelopoulos; Persado Kevin Lee; University of Chicago, Booth School of Business Sanjog Misra; University of Chicago Booth School
presented by: Kevin Lee, University of Chicago, Booth School of Business
ElicitationGPT: Text Elicitation Mechanisms via Language Models
By Yifan Wu; Northwestern University Jason Hartline; Northwestern University
presented by: Yifan Wu, Northwestern University
Decomposing Changes in the Gender Wage Gap over Worker Careers
[slides]
By Keyon Vafa; Harvard University Susan Athey; Stanford University David Blei; Columbia University
presented by: Keyon Vafa, Harvard University
Session 33: Algorithmic Collusion August 14, 2024 12:00 to 13:00 Location: Parallel 4, Room 265
Session Chair: Giacomo Mantegazza, Amazon
Session type: contributed
Algorithmic Collusion by Large Language Models
By Sara Fish; Harvard University Yannai Gonczarowski; Harvard University Ran Shorrer; Penn State
presented by: Sara Fish, Harvard University
Regulation of Algorithmic Collusion
By Jason Hartline; Northwestern University Sheng Long; Northwestern University Chenhao Zhang; Northwestern University
presented by: Chenhao Zhang, Northwestern University
Artificial Intelligence and Spontaneous Collusion
By Martino Banchio; Google Research Giacomo Mantegazza; Amazon
presented by: Giacomo Mantegazza, Amazon
Session 34: Algorithmic Decision Making and Statistical Inference August 14, 2024 12:00 to 13:00 Location: Parallel 5, Room 291
Session Chair: Jann Spiess, Stanford University
Session type: contributed
TESTING FAIRNESS-IMPROVABILITY OF ALGORITHMS
By Eric Auerbach; Northwestern University Annie Liang; Northwestern University Kyohei Okumura; Northwestern University Max Tabord-Meehan; University of Chicago
presented by: Eric Auerbach, Northwestern University
Inference for an Algorithmic Fairness-Accuracy Frontier
By Yiqi Liu; Cornell University Francesca Molinari; Cornell
presented by: Yiqi Liu, Cornell University
Unpacking the Black Box: Regulating Algorithmic Decisions
By Laura Blattner; Stanford University Scott Nelson; University of Chicago Booth School of Bu Jann Spiess; Stanford University
presented by: Jann Spiess, Stanford University
Session 35: Learning in Stackelberg Game Environment August 14, 2024 12:00 to 13:00 Location: Parallel 3, Room 198
Session Chair: Kunhe Yang, UC Berkeley
Session type: contributed
Impact of Decentralized Learning on Player Utilities in Stackelberg Games
By Kate Donahue; Cornell Nicole Immorlica; Microsoft Research Meena Jagadeesan; UC Berkeley Brendan Lucier; Microsoft Aleksandrs Slivkins; Microsoft Research
presented by: Meena Jagadeesan, UC Berkeley
Bot Beware: On the limits of algorithmic learning in monopoly markets
By
presented by: Stephan Waizmann, Yale University
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
By Nika Haghtalab; UC Berkeley Chara Podimata; MIT Kunhe Yang; UC Berkeley
presented by: Kunhe Yang, UC Berkeley
Session 36: Optimal Treatment Choice August 14, 2024 12:00 to 13:00 Location: Parallel 2, Room 165
Session Chair: Evan Munro, Stanford University
Session type: contributed
Policy Learning with Distributional Welfare
By Yifan Cui; Zhejiang University Sukjin Han; University of Bristol
presented by: Sukjin Han, University of Bristol
Optimal tests following sequential experiments
By
presented by: Karun Adusumilli, University of Pennsylvania
Treatment Allocation with Strategic Agents
By
presented by: Evan Munro, Stanford University
Session 37: Pricing using Reinforcement Learning August 14, 2024 12:00 to 13:00 Location: Parallel 6, Room 391
Session Chair: Jesse Thibodeau, Mila - Quebec AI Institute
Session type: contributed
Optimal Comprehensible Targeting
By
presented by: Walter Zhang, University of Chicago
Offline Reinforcement Learning for Pricing and Inventory Control under Censored Demand
By
presented by: Korel Gundem, George Washington University
Dynamic Incentives in Response to Dynamic Pricing
By Jesse Thibodeau; Mila - Quebec AI Institute Hadi Nekoei; Mila - Quebec AI Institute Afaf Taïk; Mila - Quebec AI Institute Janarthanan Rajendran; Dalhousie University Golnoosh Farnadi; Mila - Quebec AI Institute
presented by: Jesse Thibodeau, Mila - Quebec AI Institute
Session 38: Analysis of Online Posts August 14, 2024 14:30 to 15:50 Location: Parallel 3, Room 198
Session Chair: Carlo Schwarz, Bocconi University
Session type: contributed
High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media
By Yuya Sasaki; Vanderbilt University Jing Tao; University of Washington Yulong Wang; Syracuse University
presented by: Jing Tao, University of Washington
Learning from Viral Content
By Krishna Dasaratha; Boston University Kevin He; University of Pennsylvania
presented by: Kevin He, University of Pennsylvania
(Dis)Information Wars
[slides]
By Adrian Casillas; MIT Maryam Farboodi; Massachusetts Institute of Technology Maryam Saeedi; Carnegie Mellon University
presented by: Maryam Saeedi, Carnegie Mellon University
The Content Moderator's Dilemma: Online Plurality and the Removal of Toxic Content
By Mahyar Habibi; Bocconi University Dirk Hovy; Bocconi University Carlo Schwarz; Bocconi University
presented by: Carlo Schwarz, Bocconi University
Session 39: AI and the future of work August 14, 2024 14:30 to 15:50 Location: Parallel 5, Room 291
Session Chair: Fan Yao, University of Virginia
Session type: contributed
Artificial Intelligence, Data Corruption, and Labor Displacement
By
presented by: zhifeng cai, Rutgers University
Scenarios for the Transition to AGI
By Anton Korinek; University of Virginia Donghyun Suh; University of Virginia
presented by: Donghyun Suh, University of Virginia
From Creation to Caution: The Effect of AI on Online Art Market
By
presented by: Sijie Lin, University of Toronto
Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
By
presented by: Fan Yao, University of Virginia
Session 40: Algorithmic Decision Making and Human-AI Interaction August 14, 2024 14:30 to 15:50 Location: Parallel 1, Room 196
Session Chair: Eli Ben-Michael, CMU
Session type: contributed
On the Fairness of Machine-Assisted Human Decisions
By Talia Gillis; Columbia University Bryce McLaughlin; Stanford University Jann Spiess; Stanford University
presented by: Jann Spiess, Stanford University
Optimal and Fair Encouragement Policy Evaluation and Learning
By
presented by: Angela Zhou,
One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas
By
presented by: Vitaly Meursault, Federal Reserve Bank of Philadelphia
Does AI help humans make better decisions? A methodological framework for experimental evaluation
By Eli Ben-Michael; CMU D. James Greiner; Harvard Law School Melody Huang; UCLA Kosuke Imai; Harvard University Zhichao Jiang; University of Massacusetts Amherst Sooahn Shin; Harvard University
presented by: Eli Ben-Michael, CMU
Session 41: Analysis of non-standard data (2) August 14, 2024 14:30 to 15:50 Location: Parallel 4, Room 265
Session Chair: Timothy Christensen, Yale University
Session type: contributed
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
By Philipp Bach; University of Hamburg Victor Chernozhukov; Massachusetts Institute of Technology Sven Klaassen; University of Hamburg Martin Spindler; University of Hamburg Jan Teicher-Kluge; University of Hamburg Suhas Vijaykumar; MIT
presented by: Jan Teicher-Kluge, University of Hamburg
Demand Estimation with Text and Image Data
By Giovanni Compiani; University of Chicago ILYA MOROZOV; Northwestern University Stephan Seiler; Imperial College London
presented by: Giovanni Compiani, University of Chicago
Kernel Ridge Regression Inference, with Applications to Preference Data
By Rahul Singh; Harvard University Suhas Vijaykumar; MIT
presented by: Suhas Vijaykumar, MIT
Inference for Regression with Variables Generated from Unstructured Data
By Laura Battaglia; Oxford University Timothy Christensen; University College London Stephen Hansen; University College London Szymon Sacher; Stanford University
presented by: Timothy Christensen, Yale University
Session 42: Pricing in Markets August 14, 2024 14:30 to 15:50 Location: Parallel 6, Room 391
Session Chair: Junhui Cai, University of Notre Dame
Session type: contributed
Two-Sided Markets and Restricted Boltzmann Machines
By Tetsuya Hoshino; ITAM Romans Pancs; ITAM
presented by: Tetsuya Hoshino, ITAM
Interference Among First-Price Pacing Equilibria: A Bias and Variance Analysis
By
presented by: Luofeng Liao,
Data Market Design through Deep Learning
By Sai Srivatsa Ravindranath; Harvard University Yanchen Jiang; Harvard University David Parkes; Harvard University
presented by: Yanchen Jiang, Harvard University
Optimal Assortment and Pricing via Generalized MNL Models with Novel Poisson Arrivals
By Junhui Cai; University of Notre Dame Ran Chen; Massachusetts Institute of Technology Qitao Huang; Tsinghua University Martin Wainwright; Massachusetts Institute of Technology Linda Zhao; University of Pennsylvania Wu Zhu; Tsinghua University
presented by: Junhui Cai, University of Notre Dame
Session 43: Statistical Decisions and Experiments August 14, 2024 14:30 to 15:50 Location: Parallel 2, Room 165
Session Chair: Ethan Che, Columbia Business School
Session type: contributed
Comprehensive OOS Evaluation of Predictive Algorithms with Statistical Decision Theory
By Jeff Dominitz; NORC at the University of Chicago Charles Manski; Northwestern University
presented by: Charles Manski, Northwestern University
Decision Theory for Treatment Choice Problems with Partial Identification
By José Luis Montiel Olea; Cornell University Chen Qiu; Cornell University Joerg Stoye; Cornell University
presented by: Chen Qiu, Cornell University
Learning treatment effects while treating those in need
By Bryan Wilder; Carnegie Mellon University Pim Welle; Allegheny County Department of Human Services
presented by: Bryan Wilder, Carnegie Mellon University
Planning Batch Adaptive Experiments with Model-Predictive Control
By Ethan Che; Columbia Business School
presented by: Ethan Che, Columbia Business School
Session 44: Keynote lecture 3 August 14, 2024 16:20 to 17:50 Location: Alice Statler Auditorium
Session Chair: Eva Tardos, Cornell
Session type: invited
Machine Learning for Modeling Worker Careers
By Susan Athey; Stanford University
presented by: Susan Athey, Stanford University
Contracts, Uncertainty, and Incentives in Statistical Decision-Making
By Michael Jordan; University of California, Berkeley
presented by: Michael Jordan, University of California, Berkeley

 

This program was last updated on 2024-08-13 07:46:45 EDT