Publications

2025
Article

Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

By: Kosuke Imai and Michael Lingzhi Li

Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details

Keywords: AI and Machine Learning; Mathematical Methods; Analytics and Data Science

2016
Working Paper

Paying (for) Attention: The Impact of Information Processing Costs on Bayesian Inference

By: Scott Duke Kominers, Xiaosheng Mu and Alexander Peysakhovich

Human information processing is often modeled as costless Bayesian inference. However, research in psychology shows that attention is a computationally costly and potentially limited resource. We study a Bayesian individual for whom computing posterior beliefs is... View Details

Keywords: Behavior; Cognition and Thinking; Economics

Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, that is, with a node’s network partners being informative about the node’s attributes and therefore... View Details

2018
Conference Presentation

Learning to Recognize Objects Provides Category-orthogonal Features for Social Inference and Moral Judgment

By: J. De Freitas, A. Hafri, G. A. Alvarez and D. L. K. Yamins

Article

Aid in the Aftermath of Hurricane Katrina: Inferences of Secondary Emotions and Intergroup Helping

By: A.J.C. Cuddy, M. Rock and M. I. Norton

April 2020
Article

Regulatory Oversight, Causal Inference, and Safe and Effective Health Care Machine Learning

By: Ariel Dora Stern and W. Nicholson Price, II

In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S. Food and Drug Administration (FDA) has been engaging... View Details

Keywords: Machine Learning; Causal Inference; Health Care and Treatment; Safety; Governing Rules, Regulations, and Reforms

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data,... View Details

November 2024
Article

Preference Externality Estimators: A Comparison of Border Approaches and IVs

By: Xi Ling, Wesley R. Hartmann and Tomomichi Amano

This paper compares two estimators—the Border Approach and an Instrumental Variable (IV) estimator—using a unified framework where identifying variation arises from “preference externalities,” following the intuition in Waldfogel (2003). We highlight two dimensions in... View Details

Keywords: Econometrics; Casual Inference; Marketing; Economics; Advertising; Mathematical Methods

2008
Other Unpublished Work

User Adaptive Web Morphing: An Implementation of a Web-based Bayesian Inference Engine with Gittins Index: Master of Engineering Thesis

By: Clarence Lee

2022
Working Paper

Causal Inference During A Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina

By: Sebastian Calonico, Rafael Di Tella and Juan Cruz Lopez Del Valle

Many medical decisions during the pandemic were made without the support of causal evidence obtained in clinical trials. We study the case of nebulized ibuprofen (NaIHS), a drug that was extensively used on COVID-19 patients in Argentina amidst wild claims about its... View Details

Keywords: COVID-19; Drug Treatment; Health Pandemics; Health Care and Treatment; Decision Making; Outcome or Result; Argentina

April 2020
Article

Designs for Estimating the Treatment Effect in Networks with Interference

By: Ravi Jagadeesan, Natesh S. Pillai and Alexander Volfovsky

In this paper, we introduce new, easily implementable designs for drawing causal inference from randomized experiments on networks with interference. Inspired by the idea of matching in observational studies, we introduce the notion of considering a treatment... View Details

Keywords: Experimental Design; Network Inference; Neyman Estimator; Symmetric Interference Model; Homophily

2013
Chapter

Capturing History: The Case of the Federal Radio Commission in 1927

By: David Moss and Jonathan Lackow

In the study of regulation (and political economy more generally), there is a danger that historical inferences from theory may infect historical tests of theory. It is imperative, therefore, that historical tests always involve a vigorous search not only for... View Details

Keywords: Capture; History By Inference; Economic Theory Of Regulation; Federal Radio Commission; Theory; Economics; Media and Broadcasting Industry; United States

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Article

The Importance of Being Causal

By: Iavor I Bojinov, Albert Chen and Min Liu

Causal inference is the study of how actions, interventions, or treatments affect outcomes of interest. The methods that have received the lion’s share of attention in the data science literature for establishing causation are variations of randomized experiments.... View Details

Keywords: Causal Inference; Observational Studies; Cross-sectional Studies; Panel Studies; Interrupted Time-series; Instrumental Variables

August 2016
Article

The Role of (Dis)similarity in (Mis)predicting Others' Preferences

By: Kate Barasz, Tami Kim and Leslie K. John

Consumers readily indicate liking options that appear dissimilar—for example, enjoying both rustic lake vacations and chic city vacations or liking both scholarly documentary films and action-packed thrillers. However, when predicting other consumers’ tastes for the... View Details

Keywords: Perceived Similarity; Prediction Error; Preference Prediction; Self-other Difference; Social Inference; Cognition and Thinking; Perception; Forecasting and Prediction

2020
Working Paper

Fresh Fruit and Vegetable Consumption: The Impact of Access and Value

By: Retsef Levi, Elisabeth Paulson and Georgia Perakis

The goal of this paper is to leverage household-level data to improve food-related policies aimed at increasing the consumption of fruits and vegetables (FVs) among low-income households. Currently, several interventions target areas where residents have limited... View Details

Keywords: Food Deserts; Food Access; Food Policy; Causal Inference; Food; Nutrition; Poverty; Government Administration

SSRN

March 2022
Article

Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models

By: Fiammetta Menchetti and Iavor Bojinov

Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless... View Details

Keywords: Causal Inference; Partial Interference; Synthetic Controls; Bayesian Structural Time Series; Mathematical Methods

2023
Article

Experimental Evaluation of Individualized Treatment Rules

By: Kosuke Imai and Michael Lingzhi Li

The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a... View Details

Keywords: Causal Inference; Heterogeneous Treatment Effects; Precision Medicine; Uplift Modeling; Analytics and Data Science; AI and Machine Learning

April–June 2022
Other Article

Commentary on 'Causal Decision Making and Causal Eﬀect Estimation Are Not the Same... and Why It Matters'

By: Edward McFowland III

There has been a substantial discussion in various methodological and applied literatures around causal inference; especially in the use of machine learning and statistical models to understand heterogeneity in treatment effects and to make optimal decision... View Details

Keywords: Causal Inference; Treatment Effect Estimation; Treatment Assignment Policy; Human-in-the-loop; Decision Making; Fairness

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2024
Working Paper

Winner Take All: Exploiting Asymmetry in Factorial Designs

By: Matthew DosSantos DiSorbo, Iavor I. Bojinov and Fiammetta Menchetti

Researchers and practitioners have embraced factorial experiments to simultaneously test multiple treatments, each with different levels. With the rise of technologies like Generative AI, factorial experimentation has become even more accessible: it is easier than ever... View Details

Keywords: Factorial Designs; Fisher Randomizations; Rank Estimators; Employer Interventions; Causal Inference; Mathematical Methods; Performance Improvement