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- News (32)
- Research (357)
- Events (16)
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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.)
- 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
Bojinov, Iavor I., Albert Chen, and Min Liu. "The Importance of Being Causal." Harvard Data Science Review 2.3 (July 30, 2020).
- November 2021
- Article
Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective
By: Iavor Bojinov, Ashesh Rambachan and Neil Shephard
In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative... View Details
Keywords: Panel Data; Dynamic Causal Effects; Potential Outcomes; Finite Population; Nonparametric; Mathematical Methods
Bojinov, Iavor, Ashesh Rambachan, and Neil Shephard. "Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective." Quantitative Economics 12, no. 4 (November 2021): 1171–1196.
- 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
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.
- 2019
- Article
Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading
By: Iavor I Bojinov and Neil Shephard
We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of a broad class of these estimands and exact randomization based p-values for testing causal... View Details
Bojinov, Iavor I., and Neil Shephard. "Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading." Journal of the American Statistical Association 114, no. 528 (2019): 1665–1682.
- 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.
Importance of Being Causal
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... View Details
- 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.
- 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... View Details
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.
- 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.
- 2023
- Working Paper
Causal Interpretation of Structural IV Estimands
By: Isaiah Andrews, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan and Jesse M. Shapiro
We study the causal interpretation of instrumental variables (IV) estimands of nonlinear, multivariate structural models with respect to rich forms of model misspecification. We focus on guaranteeing that the researcher's estimator is sharp zero consistent, meaning... View Details
Keywords: Mathematical Methods
Andrews, Isaiah, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan, and Jesse M. Shapiro. "Causal Interpretation of Structural IV Estimands." NBER Working Paper Series, No. 31799, October 2023.
- 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.
- 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.
- 2011
- Article
The Causal Impact of Media in Financial Markets
By: Christopher Parsons and J. Engelberg
Disentangling the causal impact of media reporting from the impact of the events being reported is challenging. We solve this problem by comparing the behaviors of investors with access to different media coverage of the same information event. We use zip codes to... View Details
Parsons, Christopher, and J. Engelberg. "The Causal Impact of Media in Financial Markets." Journal of Finance 66, no. 1 (February 2011): 67–97.
- Forthcoming
- Chapter
Racism, Causal Explanations, and Affirmative Action
By: Theresa K. Vescio, Amy Cuddy, Faye Crosby and Kevin Weaver
BOOK ABSTRACT: In recent decades, research in political psychology has illuminated the psychological processes underlying important political action, both by ordinary citizens and by political leaders. As the world has become increasingly engaged in thinking about... View Details
Vescio, Theresa K., Amy Cuddy, Faye Crosby, and Kevin Weaver. "Racism, Causal Explanations, and Affirmative Action." Chap. 11 in Political Psychology: New Explorations, edited by Jon A. Krosnick, I-Chant Chiang, and Tobias H. Stark, 419–445. Frontiers of Social Psychology. New York: Routledge, 2016.
Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading
We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of a broad class of these estimands and exact... View Details
- January 2014
- Article
Networks and Productivity: Causal Evidence from Editor Rotations
By: J. Brogaard, J. Engelberg and Christopher Parsons
Using detailed publication and citation data for over 50,000 articles from 30 major economics and finance journals, we investigate whether network proximity to an editor influences research productivity. During an editor's tenure, his current university colleagues... View Details
Keywords: Networks; Performance Productivity; Education Industry; Journalism and News Industry; Publishing Industry
Brogaard, J., J. Engelberg, and Christopher Parsons. "Networks and Productivity: Causal Evidence from Editor Rotations." Journal of Financial Economics 111, no. 1 (January 2014): 251–270.
A Causal Test of the Strength of Weak Ties
The strength of weak ties is an influential social-scientific theory that stresses the importance of weak associations (e.g., acquaintance versus close friendship) in influencing the transmission of information through social networks. However, causal tests of... View Details
- March 2024
- Article
Human Capital Affects Religious Identity: Causal Evidence from Kenya
By: Livia Alfonsi, Michal Bauer, Julie Chytilová and Edward Miguel
We study how human capital and economic conditions causally affect the choice of religious denomination. We utilize a longitudinal dataset monitoring the religious history of more than 5,000 Kenyans over 20 years, in tandem with a randomized experiment (deworming) that... View Details
Alfonsi, Livia, Michal Bauer, Julie Chytilová, and Edward Miguel. "Human Capital Affects Religious Identity: Causal Evidence from Kenya." Art. 103215. Journal of Development Economics 167 (March 2024).
- September 16, 2022
- Article
A Causal Test of the Strength of Weak Ties
By: Karthik Rajkumar, Guillaume Saint-Jacques, Iavor I. Bojinov, Erik Brynjolfsson and Sinan Aral
The authors analyzed data from multiple large-scale randomized experiments on LinkedIn’s People You May Know algorithm, which recommends new connections to LinkedIn members, to test the extent to which weak ties increased job mobility in the world’s largest... View Details
Rajkumar, Karthik, Guillaume Saint-Jacques, Iavor I. Bojinov, Erik Brynjolfsson, and Sinan Aral. "A Causal Test of the Strength of Weak Ties." Science 377, no. 6612 (September 16, 2022).