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(451)
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Show Results For
- All HBS Web
(451)
- News (32)
- Research (357)
- Events (16)
- Multimedia (1)
- Faculty Publications (257)
- 2024
- Working Paper
Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference
By: Michael Lindon, Dae Woong Ham, Martin Tingley and Iavor I. Bojinov
Linear regression adjustment is commonly used to analyze randomized controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to... View Details
Lindon, Michael, Dae Woong Ham, Martin Tingley, and Iavor I. Bojinov. "Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference." Harvard Business School Working Paper, No. 24-060, March 2024.
- Article
Kill or Die: Moral Judgment Alters Linguistic Coding of Causality
By: Julian De Freitas, Peter DiScioli, Jason Nemirow, Maxim Massenkoff and Steven Pinker
What is the relationship between the language people use to describe an event and their moral judgments?
We test the hypothesis that moral judgment and causative verbs rely on the same underlying mental
model of people’s actions. Experiment 1a finds that participants... View Details
Keywords: Moral Cognition; Moral Psychology; Causative Verbs; Trolley Problem; Argument Structure; Moral Sensibility; Judgments
De Freitas, Julian, Peter DiScioli, Jason Nemirow, Maxim Massenkoff, and Steven Pinker. "Kill or Die: Moral Judgment Alters Linguistic Coding of Causality." Journal of Experimental Psychology: Learning, Memory, and Cognition 43, no. 8 (August 2017): 1173–1182.
- 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
Li, Michael Lingzhi, and Kosuke Imai. "Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules." Journal of Causal Inference 12, no. 1 (2024).
- Winter 2021
- Article
Can Staggered Boards Improve Value? Causal Evidence from Massachusetts
By: Robert Daines, Shelley Xin Li and Charles C.Y. Wang
We study the effect of staggered boards (SBs) using a quasi-experiment: a 1990 law that imposed an SB on all Massachusetts-incorporated firms. The law led to an increase in Tobin's Q, investment in CAPEX and R&D, patents, higher-quality patented innovations, and... View Details
Keywords: Staggered Board; Entrenchment; Life-cycle; Tobin's Q; Innovation; Profitability; Investor Composition; Governing and Advisory Boards; Investment; Innovation and Invention; Institutional Investing; Value
Daines, Robert, Shelley Xin Li, and Charles C.Y. Wang. "Can Staggered Boards Improve Value? Causal Evidence from Massachusetts." Contemporary Accounting Research 38, no. 4 (Winter 2021): 3053–3084.
- 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
Caprariello, P., A.J.C. Cuddy, and S.T. Fiske. "Social Structure Shapes Cultural Stereotypes and Emotions: A Causal Test of the Stereotype Content Model." Group Processes & Intergroup Relations 12, no. 2 (2009): 147–155.
- 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
Liang, Biyonka, and Iavor I. Bojinov. "An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits." Harvard Business School Working Paper, No. 24-057, March 2024.
Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations
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
- 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
Bahar, Dany, Prithwiraj Choudhury, Sara Signorelli, and James M. Sappenfield. "Talent Flows and the Geography of Knowledge Production: Causal Evidence from Multinational Firms." Harvard Business School Working Paper, No. 22-047, January 2022. (Revised December 2022.)
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
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
- 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
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
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
Malter, Daniel. "On the Causality and Cause of Returns to Organizational Status: Evidence from the Grands Crus Classés of the Médoc." Administrative Science Quarterly 59, no. 2 (June 2014): 271–300.
- 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
Guarin, Arlen, Christian Posso, Estefania Saravia, and Jorge Tamayo. "The Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes." Harvard Business School Working Paper, No. 22-015, February 2021. (R&R American Economic Journal.)
- 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
- 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
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.
- 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
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.
- 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
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.
- 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
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.
- 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
DosSantos DiSorbo, Matthew, Iavor I. Bojinov, and Fiammetta Menchetti. "Evaluations Amid Measurement Error: Determining the Optimal Timing for Workplace Interventions." Harvard Business School Working Paper, No. 24-075, June 2024. (Revised May 2025.)
- 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
Bertini, Marco, Daniel Halbheer, and Oded Koenigsberg. "Price and Quality Decisions by Self-Serving Managers." International Journal of Research in Marketing 37, no. 2 (June 2020): 236–257.
- 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
Bojinov, Iavor I., Kevin Wu Han, and Guillaume Basse. "Population Interference in Panel Experiments." Harvard Business School Working Paper, No. 21-100, March 2021.