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- 2023
- 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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Keywords:
Causal Inference;
Homophily;
Social Networks;
Peer Influence;
Social and Collaborative Networks;
Power and Influence;
Mathematical Methods
McFowland III, Edward, and Cosma Rohilla Shalizi. "Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations." Journal of the American Statistical Association 118, no. 541 (2023): 707–718.
- 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...
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Keywords:
Causal Inference;
Heterogeneous Treatment Effects;
Precision Medicine;
Uplift Modeling;
Analytics and Data Science;
AI and Machine Learning
Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association 118, no. 541 (2023): 242–256.
- 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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Keywords:
Authenticity;
Weaknesses;
Self-disclosure;
Leaders;
Impression Management;
Leadership Style;
Motivation and Incentives
Jiang, Li, Leslie K. John, Reihane Boghrati, and Maryam Kouchaki. "Fostering Perceptions of Authenticity via Sensitive Self-Disclosure." Journal of Experimental Psychology: Applied 28, no. 4 (December 2022): 898–915.
- 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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Keywords:
Machine Learning;
Econometric Analysis;
Instrumental Variable;
Random Forest;
Causal Inference;
AI and Machine Learning;
Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- 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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Keywords:
Causal Inference;
Causality;
Experiment;
Experimental Design;
Data Science;
Analytics and Data Science
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Causal Inference." Harvard Business School Module Note 622-111, June 2022. (Revised July 2022.)
- 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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Keywords:
COVID-19;
Drug Treatment;
Health Pandemics;
Health Care and Treatment;
Decision Making;
Outcome or Result;
Argentina
Calonico, Sebastian, Rafael Di Tella, and Juan Cruz Lopez Del Valle. "Causal Inference During A Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina." NBER Working Paper Series, No. 30084, May 2022.
- 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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Keywords:
Difference In Differences;
Staggered Difference-in-differences Designs;
Generalized Difference-in-differences;
Dynamic Treatment Effects;
Mathematical Methods
Baker, Andrew C., David F. Larcker, and Charles C.Y. Wang. "How Much Should We Trust Staggered Difference-In-Differences Estimates?" Journal of Financial Economics 144, no. 2 (May 2022): 370–395. (Editor's Choice, May 2022; Jensen Prize, First Place, June 2023.)
- 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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Keywords:
Causal Inference;
Treatment Effect Estimation;
Treatment Assignment Policy;
Human-in-the-loop;
Decision Making;
Fairness
McFowland III, Edward. "Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'." INFORMS Journal on Data Science 1, no. 1 (April–June 2022): 21–22.
- 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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Keywords:
Causal Inference;
Partial Interference;
Synthetic Controls;
Bayesian Structural Time Series;
Mathematical Methods
Menchetti, Fiammetta, and Iavor Bojinov. "Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models." Annals of Applied Statistics 16, no. 1 (March 2022): 414–435.
- 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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Bojinov, Iavor I., Kevin Wu Han, and Guillaume Basse. "Population Interference in Panel Experiments." Harvard Business School Working Paper, No. 21-100, March 2021.
- 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
Levi, Retsef, Elisabeth Paulson, and Georgia Perakis. "Fresh Fruit and Vegetable Consumption: The Impact of Access and Value." MIT Sloan Research Paper, No. 5389-18, October 2020.
- 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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Keywords:
Causal Inference;
Observational Studies;
Cross-sectional Studies;
Panel Studies;
Interrupted Time-series;
Instrumental Variables
Bojinov, Iavor I., Albert Chen, and Min Liu. "The Importance of Being Causal." Harvard Data Science Review 2.3 (July 30, 2020).
- 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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Keywords:
Machine Learning;
Causal Inference;
Health Care and Treatment;
Safety;
Governing Rules, Regulations, and Reforms
Stern, Ariel Dora, and W. Nicholson Price, II. "Regulatory Oversight, Causal Inference, and Safe and Effective Health Care Machine Learning." Biostatistics 21, no. 2 (April 2020): 363–367.
- 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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Keywords:
Moral Judgment;
Lay Decision Theory;
Theory Of Mind;
Causal Attribution;
Moral Sensibility;
Decision Making
De Freitas, Julian, and Samuel G.B. Johnson. "Optimality Bias in Moral Judgment." Journal of Experimental Social Psychology 79 (November 2018): 149–163.
- 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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Keywords:
Causal Inference;
Program Evaluation;
Algorithms;
Distributional Average Treatment Effect;
Treatment Effect Subset Scan;
Heterogeneous Treatment Effects
McFowland III, Edward, Sriram Somanchi, and Daniel B. Neill. "Efficient Discovery of Heterogeneous Treatment Effects in Randomized Experiments via Anomalous Pattern Detection." Working Paper, 2018. (2nd Round Revision.)
- 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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De Freitas, Julian, and George A. Alvarez. "Your Visual System Provides All the Information You Need to Make Moral Judgments about Generic Visual Events." Cognition 178 (September 2018): 133–146.
- 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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Ramarajan, Lakshmi, Nancy Rothbard, and Steffanie Wilk. "Discordant vs. Harmonious Selves: The Effects of Identity Conflict and Enhancement on Sales Performance in Employee-Customer Interactions." Academy of Management Journal 60, no. 6 (December 2017): 2208–2238.
- 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...
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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;
Consumer Behavior;
Health Care and Treatment;
Health Testing and Trials;
Communication Strategy;
Health Industry
Beshears, John. "Evive Health and Workplace Influenza Vaccinations." Harvard Business School Case 916-044, March 2016.
- 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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LeBoeuf, Robyn A., and Michael I. Norton. "Consequence-Cause Matching: Looking to the Consequences of Events to Infer Their Causes." Journal of Consumer Research 39, no. 1 (June 2012): 128–141.