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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

Workshop Home Page

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