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(322)
- News (19)
- Research (273)
- Events (6)
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- June 2022 (Revised January 2025)
- Technical 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... View Details
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 Technical Note 622-111, June 2022. (Revised January 2025.)
- 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.
- March 2022 (Revised January 2025)
- Technical Note
Statistical Inference
This note provides an overview of statistical inference for an introductory data science course. First, the note discusses samples and populations. Next the note describes how to calculate confidence intervals for means and proportions. Then it walks through the logic... View Details
Keywords: Data Science; Statistics; Mathematical Modeling; Mathematical Methods; Analytics and Data Science
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Statistical Inference." Harvard Business School Technical Note 622-099, March 2022. (Revised January 2025.)
- November 1990 (Revised August 1996)
- Background Note
Sampling and Statistical Inference
An introduction to sampling and statistical inference that covers the main concepts (confidence intervals, tests of statistical significance, choice of sample size) that are needed in making inferences about a population mean or percent. Includes discussion of problems... View Details
Schleifer, Arthur, Jr. "Sampling and Statistical Inference." Harvard Business School Background Note 191-092, November 1990. (Revised August 1996.)
- April 2023
- Article
Inattentive Inference
By: Thomas Graeber
This paper studies how people infer a state of the world from information structures that include additional, payoff-irrelevant states. For example, learning from a customer review about a product’s quality requires accounting for the reviewer’s otherwise irrelevant... View Details
Graeber, Thomas. "Inattentive Inference." Journal of the European Economic Association 21, no. 2 (April 2023): 560–592.
- 26 Nov 2013
- News
Inference for Proportions
- February 1994
- Background Note
Causal Inference
Discusses what causation is and what one can (and cannot) learn about causation from observational (nonexperimental) data. View Details
Schleifer, Arthur, Jr. "Causal Inference." Harvard Business School Background Note 894-032, February 1994.
- 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
Causal Inference in Accounting Research
By: Ian D. Gow, David F. Larcker and Peter C. Reiss
This paper examines the approaches accounting researchers use to draw causal inferences using observational (or non-experimental) data. The vast majority of accounting research papers draws causal inferences notwithstanding the well-known difficulties in doing so with... View Details
Gow, Ian D., David F. Larcker, and Peter C. Reiss. "Causal Inference in Accounting Research." Journal of Accounting Research 54, no. 2 (May 2016): 477–523.
- 2023
- Working Paper
Design-Based Inference for Multi-arm Bandits
By: Dae Woong Ham, Iavor I. Bojinov, Michael Lindon and Martin Tingley
Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online decision-making. Consequently, there is an increased desire to use the available... View Details
Ham, Dae Woong, Iavor I. Bojinov, Michael Lindon, and Martin Tingley. "Design-Based Inference for Multi-arm Bandits." Harvard Business School Working Paper, No. 24-056, March 2024.
- 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... View Details
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.
- 1979
- Dissertation
Inference in Partially Identified Models
By: Dutch Leonard
Keywords: Mathematical Methods
- 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
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.
- August 2003
- Background Note
Drawing Inferences from the Written Interview
Provides instructions for analyzing the "Written Interview" assignment. View Details
Higgins, Monica C. "Drawing Inferences from the Written Interview." Harvard Business School Background Note 404-012, August 2003.
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first... View Details
Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li. "Distributionally Robust Causal Inference with Observational Data." Working Paper, February 2023.
- June 2014
- Article
The Red Sneakers Effect: Inferring Status and Competence from Signals of Nonconformity
By: Silvia Bellezza, Francesca Gino and Anat Keinan
We examine how people react to nonconforming behaviors, such as entering a luxury boutique wearing gym clothes rather than an elegant outfit or wearing red sneakers in a professional setting. Nonconforming behaviors, as costly and visible signals, can act as a... View Details
Bellezza, Silvia, Francesca Gino, and Anat Keinan. "The Red Sneakers Effect: Inferring Status and Competence from Signals of Nonconformity." Journal of Consumer Research 41, no. 1 (June 2014): 35–54. (Finalist, 2017 Best Article Award for a paper published in JCR in 2014.))
- Article
Motivated Inferences of Price and Quality in Healthcare Decisions
By: Emily Prinsloo, Kate Barasz and Peter A. Ubel
Policy makers have increasingly advocated for healthcare price transparency, whereby prices are made salient before services are rendered. While such policies may empower consumers, they also bring price to the forefront of healthcare choices as never before, with yet... View Details
Keywords: Healthcare; Price Transparency; Health Care and Treatment; Price; Quality; Perception; Consumer Behavior; Decisions; Insurance
Prinsloo, Emily, Kate Barasz, and Peter A. Ubel. "Motivated Inferences of Price and Quality in Healthcare Decisions." Special Issue on Healthcare and Medical Decision Making edited by Dipankar Chakravarti, Jian Ni, Meng Zhu. Journal of the Association for Consumer Research 7, no. 2 (April 2022): 186–197.
- 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.
- August 1993
- Article
Transaction Cost Theory: Inferences from Clinical Field Research
Cespedes, Frank V. "Transaction Cost Theory: Inferences from Clinical Field Research." Organization Science 4, no. 3 (August 1993): 454–477.
- October 2009
- Article
Negotiation Analysis: From Games to Inferences to Decisions to Deals
Exemplified by the pioneering work of Howard Raiffa and often expressed in the pages of the Negotiation Journal, the emergent prescriptive field of "negotiation analysis" progressively developed from Raiffa's early contributions to game theory and to his later... View Details
Keywords: Decision Choices and Conditions; Negotiation Participants; Negotiation Preparation; Negotiation Process; Negotiation Tactics; Game Theory
Sebenius, James K. "Negotiation Analysis: From Games to Inferences to Decisions to Deals." Negotiation Journal 25, no. 4 (October 2009): 449–465.