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2024
Article

Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules

By: Michael Lingzhi Li and Kosuke Imai

A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across... View Details

Keywords: AI and Machine Learning; Research

2023
Working Paper

An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits

By: Biyonka Liang and Iavor I. Bojinov

Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the... View Details

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

2009
Article

Social Structure Shapes Cultural Stereotypes and Emotions: A Causal Test of the Stereotype Content Model

By: P. Caprariello, A.J.C. Cuddy and S.T. Fiske

The stereotype content model (SCM) posits that social structure predicts specific cultural stereotypes and associated emotional prejudices (Fiske et al., 2002). No prior evidence at a societal level has manipulated both structural predictors and measured both... View Details

Keywords: Competency and Skills; Mathematical Methods; Emotions; Personal Characteristics; Prejudice and Bias; Status and Position; Culture; Competition

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2021
Working Paper

The Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes

By: Arlen Guarin, Christian Posso, Estefania Saravia and Jorge Tamayo

Identifying the effect of physicians’ skills on health outcomes is a challenging task due to the nonrandom sorting between physicians and hospitals. We overcome this challenge by exploiting a Colombian government program that randomly assigned 2,126 physicians to 618... View Details

Keywords: Physicians' Health Skills; Health Birth Outcomes; Birthing Outcomes; Experimental Evidence; Health Care and Treatment; Competency and Skills; Outcome or Result; Health Industry; Colombia

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

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2022
Working Paper

Talent Flows and the Geography of Knowledge Production: Causal Evidence from Multinational Firms

By: Dany Bahar, Prithwiraj Choudhury, Sara Signorelli and James M. Sappenfield

Leveraging a unique dataset merging patent data with all work-related migration reforms that took place in 15 countries over 26 years, we show that reforms discouraging inventor mobility decrease the patenting of MNE subsidiaries within a country, while reforms... View Details

Keywords: Migration; Technology; Policy Evaluation; Patents; Information Technology; Immigration; Policy; Collaborative Innovation and Invention; Globalization

June 2012
Article

Consequence-Cause Matching: Looking to the Consequences of Events to Infer Their Causes

By: Robyn A. LeBoeuf and Michael I. Norton

We show that people non-normatively infer event causes from event consequences. For example, people inferred that a product failure (computer crash) had a large cause (widespread computer virus) if it had a large consequence (job loss), but that the identical failure... View Details

Keywords: Causal Inference; Product; Forecasting and Prediction; Motivation and Incentives; Failure

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Article

On the Causality and Cause of Returns to Organizational Status: Evidence from the Grands Crus Classés of the Médoc

By: Daniel Malter

This paper identifies the causal symbolic effect of status on the prices organizations charge for their products. I exploit the classification of the châteaux of the Médoc, which sorted 61 wine producers into five growth classes in 1855, as a fixed hierarchical symbol... View Details

Keywords: Organizational Status; Quality Signals; Conspicuous Consumption; Wine Classification Of 1855; Grand Cru; Status and Position; Quality; Reputation; Price; France

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22 Oct 2020
Working Paper Summaries

Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models

Keywords: by Fiammetta Menchetti and Iavor Bojinov

Article

Your Visual System Provides All the Information You Need to Make Moral Judgments about Generic Visual Events

By: Julian De Freitas and George A. Alvarez

To what extent are people's moral judgments susceptible to subtle factors of which they are unaware? Here we show that we can change people’s moral judgments outside of their awareness by subtly biasing perceived causality. Specifically, we used subtle visual... View Details

Keywords: Moral Judgment; Perceived Causality; Visual Illusions; Moral Sensibility; Judgments

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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

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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

2025
Working Paper

Evaluations Amid Measurement Error: Determining the Optimal Timing for Workplace Interventions

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

Researchers have embraced factorial experiments to simultaneously evaluate multiple treatments, each with different levels. Typically, in large-scale factorial experiments, the primary objective is identifying the treatment with the largest causal effect, especially... View Details

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

2021
Working Paper

Population Interference in Panel Experiments

By: Iavor I Bojinov, Kevin Wu Han and Guillaume Basse

The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit's outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in... View Details

Keywords: Finite Population; Potential Outcomes; Dynamic Causal Effects; Mathematical Methods

Article

Price and Quality Decisions by Self-Serving Managers

By: Marco Bertini, Daniel Halbheer and Oded Koenigsberg

We present a theory of price and quality decisions by managers who are self-serving. In the theory, firms stress the price or quality of their products, but not both. Accounting for this, managers exploit any uncertainty about the cause of market outcomes to credit... View Details

Keywords: Causal Reasoning; Self-serving Bias; Strategic Orientation; Managerial Decision-making; Price; Quality; Decision Making; Theory

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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

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Article

Optimality Bias in Moral Judgment

By: Julian De Freitas and Samuel G.B. Johnson

We often make decisions with incomplete knowledge of their consequences. Might people nonetheless expect others to make optimal choices, despite this ignorance? Here, we show that people are sensitive to moral optimality: that people hold moral agents accountable... View Details

Keywords: Moral Judgment; Lay Decision Theory; Theory Of Mind; Causal Attribution; Moral Sensibility; Decision Making

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2023
Working Paper

Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection

By: Edward McFowland III, Sriram Somanchi and Daniel B. Neill

In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention’s effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides... View Details

Keywords: Causal Inference; Program Evaluation; Algorithms; Distributional Average Treatment Effect; Treatment Effect Subset Scan; Heterogeneous Treatment Effects

March 2016
Case

Evive Health and Workplace Influenza Vaccinations

By: John Beshears

Evive Health is a company that manages communication campaigns on behalf of health insurance plans and large employers. Using big data techniques and insights from behavioral economics, Evive deploys targeted and effective messages that improve individuals' health... View Details

Keywords: Vaccination; Influenza; Flu Shot; Preventive Care; Health Care; Behavioral Economics; Choice Architecture; Nudge; Experimental Design; Randomized Controlled Trial; RCT; Causal Inference; Consumer Behavior; Health Care and Treatment; Health Testing and Trials; Communication Strategy; Health Industry

March 2016 (Revised March 2022)
Teaching Note

Evive Health and Workplace Influenza Vaccinations

By: John Beshears

Evive Health is a company that manages communication campaigns on behalf of health insurance plans and large employers. Using big data techniques and insights from behavioral economics, Evive deploys targeted and effective messages that improve individuals' health... View Details

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