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    • Faculty Publications  (24)

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    • All HBS Web  (68)
      • Faculty Publications  (24)

      Causal Inference Remove Causal Inference →

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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...  View Details
      Keywords: Authenticity; Weaknesses; Self-disclosure; Leaders; Impression Management; Leadership Style; Motivation and Incentives
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      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...  View Details
      Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
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      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

      By: Iavor Bojinov, Michael Parzen and Paul Hamilton
      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...  View Details
      Keywords: Causal Inference; Causality; Experiment; Experimental Design; Data Science; Analytics and Data Science
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      Bojinov, Iavor, 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...  View Details
      Keywords: COVID-19; Drug Treatment; Health Pandemics; Health Care and Treatment; Decision Making; Outcome or Result; Argentina
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      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...  View Details
      Keywords: Difference In Differences; Staggered Difference-in-differences Designs; Generalized Difference-in-differences; Dynamic Treatment Effects; Mathematical Methods
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      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.)
      • 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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      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...  View Details
      Keywords: Finite Population; Potential Outcomes; Dynamic Causal Effects; Mathematical Methods
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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...  View Details
      Keywords: Food Deserts; Food Access; Food Policy; Causal Inference; Food; Nutrition; Poverty; Government Administration
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      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....  View Details
      Keywords: Causal Inference; Observational Studies; Cross-sectional Studies; Panel Studies; Interrupted Time-series; Instrumental Variables
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      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...  View Details
      Keywords: Machine Learning; Causal Inference; Health Care and Treatment; Safety; Governing Rules, Regulations, and Reforms
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      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...  View Details
      Keywords: Moral Judgment; Lay Decision Theory; Theory Of Mind; Causal Attribution; Moral Sensibility; Decision Making
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      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...  View Details
      Keywords: Causal Inference; Program Evaluation; Algorithms; Distributional Average Treatment Effect; Treatment Effect Subset Scan; Heterogeneous Treatment Effects
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      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...  View Details
      Keywords: Moral Judgment; Perceived Causality; Visual Illusions; Moral Sensibility; Judgments
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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,...  View Details
      Keywords: Identity; Interpersonal Communication; Sales; Performance
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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...  View Details
      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
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      Beshears, John. "Evive Health and Workplace Influenza Vaccinations." Harvard Business School Teaching Note 916-049, March 2016. (Revised March 2022.)
      • 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
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      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...  View Details
      Keywords: Causal Inference; Product; Forecasting and Prediction; Motivation and Incentives; 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.
      • February 1994
      • Background Note

      Causal Inference

      By: Arthur Schleifer Jr.
      Discusses what causation is and what one can (and cannot) learn about causation from observational (nonexperimental) data.  View Details
      Keywords: Decision Making; Analytics and Data Science; Interests
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      Schleifer, Arthur, Jr. "Causal Inference." Harvard Business School Background Note 894-032, February 1994.
      • 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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      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).
      • 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...  View Details
      Keywords: Causal Inference; Homophily; Social Networks; Peer Influence; Social and Collaborative Networks; Power and Influence; Mathematical Methods
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      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 (forthcoming). (Pre-published online August 24, 2021.)
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