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- Summer 2020
- Article
Want to Make Better Decisions? Start Experimenting
By: Michael Luca and Max Bazerman
Four lessons for using randomized controlled experiments to create value for your company and customers View Details
Luca, Michael, and Max Bazerman. "Want to Make Better Decisions? Start Experimenting." MIT Sloan Management Review 61, no. 4 (Summer 2020).
- 2022
- Working Paper
Do Startups Benefit from Their Investors' Reputation? Evidence from a Randomized Field Experiment
By: Shai Benjamin Bernstein, Kunal Mehta, Richard Townsend and Ting Xu
We analyze a field experiment conducted on AngelList Talent, a large online search platform for startup jobs. In the experiment, AngelList randomly informed job seekers of whether a startup was funded by a top-tier investor and/or was funded recently. We find that the... View Details
Keywords: Startup Labor Market; Investors; Randomized Field Experiment; Certification Effect; Venture Capital; Business Startups; Human Capital; Job Search; Reputation
Bernstein, Shai Benjamin, Kunal Mehta, Richard Townsend, and Ting Xu. "Do Startups Benefit from Their Investors' Reputation? Evidence from a Randomized Field Experiment." Harvard Business School Working Paper, No. 22-060, February 2022.
- Fall 2018
- Article
The Value of Fit Information in Online Retail: Evidence from a Randomized Field Experiment
By: Santiago Gallino and Antonio Moreno
Online channels generate frictions when selling products with nondigital attributes, such as apparel. Customers may be reluctant to purchase products they have not been able to try on, and those customers who do purchase may return products when they do not fit as... View Details
Keywords: Supply Chain Information; Fit Uncertainty; Online Retail; Randomized Field Experiment; Virtual Fitting Room; Digital Retail; Customization and Personalization; Internet and the Web; Value; Performance Improvement; Apparel and Accessories Industry; Retail Industry
Gallino, Santiago, and Antonio Moreno. "The Value of Fit Information in Online Retail: Evidence from a Randomized Field Experiment." Manufacturing & Service Operations Management 20, no. 4 (Fall 2018): 767–787.
- August 2007
- Article
Remedying Education: Evidence from Two Randomized Experiments in India
By: A. Banerjee, Shawn A. Cole, E. Duflo and L. Linden
This paper presents the results of two randomized experiments conducted in schools in urban India. A remedial education program hired young women to teach students lagging behind in basic literacy and numeracy skills. It increased average test scores of all children in... View Details
Banerjee, A., Shawn A. Cole, E. Duflo, and L. Linden. "Remedying Education: Evidence from Two Randomized Experiments in India." Quarterly Journal of Economics 122, no. 3 (August 2007): 1235–1264.
- 27 Oct 2020
- Working Paper Summaries
Does Venture Capital Attract Human Capital? Evidence from a Randomized Field Experiment
- 26 Mar 2013
- Working Paper Summaries
How Elastic Are Preferences for Redistribution? Evidence from Randomized Survey Experiments
- 2017
- Working Paper
Biased Beliefs About Random Samples: Evidence from Two Integrated Experiments
By: Daniel J. Benjamin, Don A. Moore and Matthew Rabin
This paper describes results of a pair of incentivized experiments on biases in judgments about random samples. Consistent with the Law of Small Numbers (LSN), participants exaggerated the likelihood that short sequences and random subsets of coin flips would be... View Details
Benjamin, Daniel J., Don A. Moore, and Matthew Rabin. "Biased Beliefs About Random Samples: Evidence from Two Integrated Experiments." NBER Working Paper Series, No. 23927, October 2017.
- 2024
- Working Paper
The Effects of Medical Debt Relief: Evidence from Two Randomized Experiments
By: Raymond Kluender, Neale Mahoney, Francis Wong and Wesley Yin
Two in five Americans have medical debt, nearly half of whom owe at least $2,500. Concerned by this burden, governments and private donors have undertaken large, high-profile efforts to relieve medical debt. We partnered with RIP Medical Debt to conduct two randomized... View Details
Kluender, Raymond, Neale Mahoney, Francis Wong, and Wesley Yin. "The Effects of Medical Debt Relief: Evidence from Two Randomized Experiments." NBER Working Paper Series, No. 32315, April 2024.
- Article
How Elastic Are Preferences for Redistribution? Evidence from Randomized Survey Experiments
By: Ilyana Kuziemko, Michael I. Norton, Emmanuel Saez and Stefanie Stantcheva
We analyze randomized online survey experiments providing interactive, customized information on U.S. income inequality, the link between top income tax rates and economic growth, and the estate tax. The treatment has large effects on views about inequality but only... View Details
Kuziemko, Ilyana, Michael I. Norton, Emmanuel Saez, and Stefanie Stantcheva. "How Elastic Are Preferences for Redistribution? Evidence from Randomized Survey Experiments." American Economic Review 105, no. 4 (April 2015): 1478–1508.
- 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.
- July 2023
- Article
So, Who Likes You? Evidence from a Randomized Field Experiment
By: Ravi Bapna, Edward McFowland III, Probal Mojumder, Jui Ramaprasad and Akhmed Umyarov
With one-third of marriages in the United States beginning online, online dating platforms have become important curators of the modern social fabric. Prior work on online dating has elicited two critical frictions in the heterosexual dating market. Women, governed by... View Details
Keywords: Online Dating; Internet and the Web; Analytics and Data Science; Gender; Emotions; Social and Collaborative Networks
Bapna, Ravi, Edward McFowland III, Probal Mojumder, Jui Ramaprasad, and Akhmed Umyarov. "So, Who Likes You? Evidence from a Randomized Field Experiment." Management Science 69, no. 7 (July 2023): 3939–3957.
- Article
Attracting Early Stage Investors: Evidence from a Randomized Field Experiment
By: S. Bernstein, A. Korteweg and K. Laws
Bernstein, S., A. Korteweg, and K. Laws. "Attracting Early Stage Investors: Evidence from a Randomized Field Experiment." Journal of Finance 72, no. 2 (April 2017): 509–538. (Lead Article.)
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
- 2023
- Working Paper
Efficient Discovery of Heterogeneous Quantile 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
McFowland III, Edward, Sriram Somanchi, and Daniel B. Neill. "Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection." Working Paper, 2023.
- 2020
- Working Paper
Design and Analysis of Switchback Experiments
By: Iavor I Bojinov, David Simchi-Levi and Jinglong Zhao
In switchback experiments, a firm sequentially exposes an experimental unit to a random treatment, measures its response, and repeats the procedure for several periods to determine which treatment leads to the best outcome. Although practitioners have widely adopted... View Details
Bojinov, Iavor I., David Simchi-Levi, and Jinglong Zhao. "Design and Analysis of Switchback Experiments." Harvard Business School Working Paper, No. 21-034, September 2020.
- 2025
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.
- July 2023
- Article
Design and Analysis of Switchback Experiments
By: Iavor I Bojinov, David Simchi-Levi and Jinglong Zhao
In switchback experiments, a firm sequentially exposes an experimental unit to a random treatment, measures its response, and repeats the procedure for several periods to determine which treatment leads to the best outcome. Although practitioners have widely adopted... View Details
Bojinov, Iavor I., David Simchi-Levi, and Jinglong Zhao. "Design and Analysis of Switchback Experiments." Management Science 69, no. 7 (July 2023): 3759–3777.
- 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.
- Forthcoming
- Article
Engaging Customers with AI in Online Chats: Evidence from a Randomized Field Experiment
By: Shunyuan Zhang and Das Narayandas
We examine how artificial intelligence (AI) affected the productivity of customer service agents and customer sentiment in online interactions. Collaborating with a meal delivery company, we conducted a randomized field experiment that exploited exogenous variation in... View Details
- 2020
- Book
The Power of Experiments: Decision-Making in a Data-Driven World
By: Michael Luca and Max H. Bazerman
Have you logged into Facebook recently? Searched for something on Google? Chosen a movie on Netflix? If so, you've probably been an unwitting participant in a variety of experiments—also known as randomized controlled trials—designed to test the impact of changes to an... View Details
Keywords: Experiments; Randomized Controlled Trials; Organizations; Decision Making; Analytics and Data Science; Management Analysis, Tools, and Techniques
Luca, Michael, and Max H. Bazerman. The Power of Experiments: Decision-Making in a Data-Driven World. Cambridge, MA: MIT Press, 2020.