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- December 2022
- Article
Fostering Perceptions of Authenticity via Sensitive Self-Disclosure
By: Li Jiang, Leslie K. John, Reihane Boghrati and Maryam Kouchaki
Leaders’ perceived authenticity—the sense that leaders are acting in accordance with their “true self”—is associated with positive outcomes for both employees and organizations alike. How might leaders foster this impression? We show that sensitive self-disclosure, in...
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- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
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, followed...
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- June 2022 (Revised July 2022)
- Module Note
Causal Inference
This note provides an overview of causal inference for an introductory data science course. First, the note discusses observational studies and confounding variables. Next the note describes how randomized experiments can be used to account for the effect of...
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- 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...
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- Article
How Much Should We Trust Staggered Difference-In-Differences Estimates?
By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang
We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in finance, accounting, and law that...
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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...
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- 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...
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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...
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Keywords:
Food Deserts;
Food Access;
Food Policy;
Causal Inference;
Food;
Nutrition;
Poverty;
Government Administration
- 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....
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- 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...
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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...
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- 2018
- Working Paper
Efficient Discovery of Heterogeneous 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...
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- 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...
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- December 2017
- Article
Discordant vs. Harmonious Selves: The Effects of Identity Conflict and Enhancement on Sales Performance in Employee-Customer Interactions
By: Lakshmi Ramarajan, Nancy Rothbard and Steffanie Wilk
Across multiple studies, we examine how identity conflict and enhancement within people affect performance in tasks that involve interactions between people through two mechanisms: role-immersion, operationalized as intrinsic motivation, and role-taking,...
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- 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...
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Keywords:
Vaccination;
Influenza;
Flu Shot;
Preventive Care;
Health Care;
Behavioral Economics;
Choice Architecture;
Nudge;
Experimental Design;
Randomized Controlled Trial;
RCT;
Causal Inference;
Health Care and Treatment;
Insurance;
Health;
Consumer Behavior;
Health Testing and Trials;
Communication Strategy;
Insurance Industry;
Health Industry
- 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
- 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...
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- February 1994
- Background Note
Causal Inference
Discusses what causation is and what one can (and cannot) learn about causation from observational (nonexperimental) data.
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- April–June 2022
- Other Article
Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'
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...
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- Forthcoming
- Article
Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations.
By: Edward McFowland III and Cosma Rohilla Shalizi
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 its...
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