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Mon Dec 13 05:00 AM -- 12:10 PM (PST)
Machine Learning Meets Econometrics (MLECON)
David Bruns-Smith · Arthur Gretton · Limor Gultchin · Niki Kilbertus · Krikamol Muandet · Evan Munro · Angela Zhou

The Machine Learning Meets Econometrics (MLECON) workshop will serve as an interface for researchers from machine learning and econometrics to understand challenges and recognize opportunities that arise from the synergy between these two disciplines as well as to exchange new ideas that will help propel the fields. Our one-day workshop will consist of invited talks from world-renowned experts, shorter talks from contributed authors, a Gather.Town poster session, and an interdisciplinary panel discussion. To encourage cross-over discussion among those publishing in different venues, the topic of our panel discussion will be “Machine Learning in Social Systems: Challenges and Opportunities from Program Evaluation”. It was designed to highlight the complexity of evaluating social and economic programs as well as shortcomings of current approaches in machine learning and opportunities for methodological innovation. These challenges include more complex environments (markets, equilibrium, temporal considerations) and behavior (heterogeneity, delayed effects, unobserved confounders, strategic response). Our team of organizers and program committees is diverse in terms of gender, race, affiliations, country of origin, disciplinary background, and seniority levels. We aim to convene a broad variety of viewpoints on methodological axes (nonparametrics, machine learning, econometrics) as well as areas of application. Our invited speakers and panelists are leading experts in their respective fields and span far beyond the core NeurIPS community. Lastly, we expect participants with diverse backgrounds from various sub-communities of machine learning and econometrics (e.g., non- and semi-parametric econometrics, applied econometrics, reinforcement learning, kernel methods, deep learning, micro- and macro-economics) among other related communities.

Welcome and Introduction (Introduction)
Invited talk #1 (Invited talk)
Invited talk #2 (Invited talk)
Coffee Break (Break)
Contributed talks Session 1 (Contributed talk)
Contributed talks Session 2 (Contributed talk)
Invited talk #3 (Invited talk)
Invited talk #4 (Invited talk)
Coffee Break (Break)
Zoom Q&A for Invited Talk #1 and #2 (Discussion)
Zoom Q&A for Contributed talks Session 1+2 (Discussion)
Zoom Q&A for Invited Talks #3 and #4 (Discussion)
Coffee Break (Break)
Poster Session 1 (Poster session)
Contributed talks Session 3 (Contributed talk)
Zoom Q&A for Contributed talks Session 3 (Discussion)
Panel Discussion: “Machine Learning in Social Systems: Challenges and Opportunities from Program Evaluation” (Discussion)
Poster Session 2 (Poster session)
Wrapup (Introduction)
Deep Causal Inequalities: Demand Estimation in Differentiated Products Markets (Poster)
Optimal design of interventions in complex socio-economic systems (Poster)
Robust Algorithms for GMM Estimation: A Finite Sample Viewpoint (Poster)
An Outcome Test of Discrimination for Ranked Lists (Poster)
Boosting engagement in ed tech with personalized recommendations (Poster)
Inference of Heterogeneous Treatment Effects Using Observational Data with High-Dimensional Covariates (Poster)
Estimation and Inference of Semiparametric Single-Index Models with High-Dimensional Covariates (Poster)
How informative is the Order Book Beyond the Best Levels? Machine Learning Perspective (Poster)
Unsupervised Feature Extraction Clustering for Crisis Prediction (Poster)
Off-Policy Evaluation with General Logging Policies (Poster)
Adaptive maximization of social welfare (Poster)
On Parameter Estimation in Unobserved Components Models subject to Linear Inequality Constraints (Poster)
Learning Causal Relationships from Conditional Moment Restrictions by Importance Weighting (Poster)
Deep Vector Autoregression for Macroeconomic Data (Poster)
Safe Online Bid Optimization with Uncertain Return-On-Investment and Budget Constraints (Poster)
Causal Gradient Boosting: Boosted Instrumental Variable Regression (Poster)
A Bayesian take on option pricing with Gaussian processes (Poster)
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy (Poster)
Quasi-Bayesian Dual Instrumental Variable Regression (Poster)
Efficient Online Estimation of Causal Effects by Deciding What to Observe (Poster)
Many Proxy Controls (Poster)
Double machine learning for sample selection models (Poster)
Evolution of topics in central bank speech communication (Poster)
Causal Matrix Completion (Poster)
Policy learning under ambiguity (Poster)
Modeling Worker Career Trajectories with Neural Sequence Models (Poster)