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- March 2020 (Revised March 2023)
- Module Note
The Role of Experiments in Organizations
By: Michael Luca
This note outlines the structure and content of a four-class module—The Role of Experiments in Organizations—that is designed to introduce students to the role of experimental methods in managerial decisions. View Details
Luca, Michael. "The Role of Experiments in Organizations." Harvard Business School Module Note 920-044, March 2020. (Revised March 2023.)
- Article
Gathering Data for Archival, Field, Survey, and Experimental Accounting Research
By: Robert Bloomfield, Mark W. Nelson and Eugene F. Soltes
In the published proceedings of the first Journal of Accounting Research Conference, Vatter (1966) lamented that “Gathering direct and original facts is a tedious and difficult task, and it is not surprising that such work is avoided.” For the 50th JAR Conference,... View Details
Keywords: Archival; Data; Experiment; Empirical Methods; Field Study; Analytics and Data Science; Surveys; Financial Reporting
Bloomfield, Robert, Mark W. Nelson, and Eugene F. Soltes. "Gathering Data for Archival, Field, Survey, and Experimental Accounting Research." Journal of Accounting Research 54, no. 2 (May 2016): 341–395.
The Experimentation Machine
Leverage AI to be a 10x Founder
Today’s most successful founders know that the startups that learn the fastest will win. In The Experimentation Machine, HBS professor, entrepreneur, and venture capitalist Jeffrey J. Bussgang reveals... View Details
- December 2016
- Article
The Effects of Endowment Size and Strategy Method on Third Party Punishment
By: Jillian J. Jordan, Katherine McAuliffe and David G. Rand
Numerous experiments have shown that people often engage in third-party punishment (3PP) of selfish behavior. This evidence has been used to argue that people respond to selfishness with anger, and get utility from punishing those who mistreat others. Elements of the... View Details
Keywords: Third-party Punishment; Norm-enforcement; Strategy Method; Economic Games; Cooperation; Emotions; Fairness
Jordan, Jillian J., Katherine McAuliffe, and David G. Rand. "The Effects of Endowment Size and Strategy Method on Third Party Punishment." Experimental Economics 19, no. 4 (December 2016): 741–763.
- 04 Dec 2019
- Book
Creating the Experimentation Organization
subtle tweaks to everything from varying shades of color to alternative placement of links and menu options for booking properties. It’s part of an innovative culture of experimentation that pervades every aspect of how the company... View Details
Keywords: by Michael Blanding
- 20 May 2020
- News
Experimentation and Its Discontents
- 2022
- Article
Data Poisoning Attacks on Off-Policy Evaluation Methods
By: Elita Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin and Himabindu Lakkaraju
Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes domains such as healthcare, where exploration is often infeasible, unethical, or expensive. However, the extent to which such methods can be trusted under adversarial threats... View Details
Lobo, Elita, Harvineet Singh, Marek Petrik, Cynthia Rudin, and Himabindu Lakkaraju. "Data Poisoning Attacks on Off-Policy Evaluation Methods." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 38th (2022): 1264–1274.
- Research Summary
Moral Reasoning & Experimental Political Philosophy
In this work, we demonstrate a new and morally significant effect on judgment and decision-making. This research is inspired by the work of John Rawls, widely regarded as the most important political philosopher of the 20th Century. Here we apply the central... View Details
- March 2016
- Supplement
Advertising Experiments at RestaurantGrades
By: Weijia Dai, Hyunjin Kim and Michael Luca
This exercise provides students with a data set consisting of results from a hypothetical experiment, and asks students to make recommendations based on the data. Through this process, the exercise teaches students to analyze, design, and interpret experiments. The... View Details
- Forthcoming
- Book
The Experimentation Machine: Finding Product–Market Fit in the Age of AI
Leverage AI to be a 10x Founder
Today’s most successful founders know that the startups that learn the fastest will win. In The Experimentation Machine, I reveal how AI is transforming the way startups find product-market fit and scale.... View Details
Today’s most successful founders know that the startups that learn the fastest will win. In The Experimentation Machine, I reveal how AI is transforming the way startups find product-market fit and scale.... View Details
Keywords: AI; Founder; Startup; AI and Machine Learning; Technology Adoption; Business Startups; Entrepreneurship; Market Entry and Exit
Bussgang, Jeffrey J. The Experimentation Machine: Finding Product–Market Fit in the Age of AI. Damn Gravity Media, forthcoming.
- 2022
- Conference Presentation
Towards the Unification and Robustness of Post hoc Explanation Methods
By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two... View Details
Keywords: AI and Machine Learning
Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Post hoc Explanation Methods." Paper presented at the 3rd Symposium on Foundations of Responsible Computing (FORC), 2022.
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- April 2020
- Article
Field Comparisons of Incentive-Compatible Preference Elicitation Techniques
By: Shawn A. Cole, A. Nilesh Fernando, Daniel Stein and Jeremy Tobacman
Knowledge of consumer demand is important for firms, policy makers, and economists. One common tool for incentive-compatible demand elicitation, the Becker-DeGroot-Marschak (BDM) mechanism, has been widely used in laboratory settings but rarely evaluated for... View Details
Keywords: Incentive-compatible Elicitation; Experimental Methods; Weather Insurance; Rainfall Insurance; Agricultural Extension; Demand and Consumers
Cole, Shawn A., A. Nilesh Fernando, Daniel Stein, and Jeremy Tobacman. "Field Comparisons of Incentive-Compatible Preference Elicitation Techniques." Journal of Economic Behavior & Organization 172 (April 2020): 33–56.
- August 2020
- Technical Note
Comparing Two Groups: Sampling and t-Testing
This note describes sampling and t-tests, two fundamental statistical concepts. View Details
Keywords: Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Analytics and Data Science; Analysis; Surveys; Mathematical Methods
Bojinov, Iavor I., Chiara Farronato, Yael Grushka-Cockayne, Willy C. Shih, and Michael W. Toffel. "Comparing Two Groups: Sampling and t-Testing." Harvard Business School Technical Note 621-044, August 2020.
- 2023
- Working Paper
Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation
By: Dae Woong Ham, Michael Lindon, Martin Tingley and Iavor Bojinov
Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. In addition to augmenting managers’ decision-making, experimentation mitigates risk by limiting the proportion of customers exposed to... View Details
Keywords: Performance Evaluation; Research and Development; Analytics and Data Science; Consumer Behavior
Ham, Dae Woong, Michael Lindon, Martin Tingley, and Iavor Bojinov. "Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation." Harvard Business School Working Paper, No. 23-070, May 2023.
- July 1999
- Background Note
Note on Statistical Tests for a Randomized Matched Pair Experimental Design, A
By: Alvin J. Silk
Concerns understanding the conditions under which an experimental design that employs matching and randomization may result in gains in precision as compared to a design that utilizes randomization and independent samples--i.e., no matching. An empirical example is... View Details
- 2014
- Working Paper
The Psycho-Social Benefits of Access to Contraception: Experimental Evidence from Zambia
By: Nava Ashraf, Marric Buessing, Erica Field and Jessica Leight
In a field experiment in Lusaka, Zambia, married couples in the catchment area of a family planning clinic were randomly assigned to either a treatment group (N=503) or a control group (N=768). Those in the treatment group received vouchers guaranteeing free and... View Details
Ashraf, Nava, Marric Buessing, Erica Field, and Jessica Leight. "The Psycho-Social Benefits of Access to Contraception: Experimental Evidence from Zambia." Working Paper, August 2014. (Under review.)
- 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.
- 2023
- Working Paper
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
By: Daniel Yue, Paul Hamilton and Iavor Bojinov
Predictive model development is understudied despite its centrality in modern artificial
intelligence and machine learning business applications. Although prior discussions
highlight advances in methods (along the dimensions of data, computing power, and
algorithms)... View Details
Keywords: Analytics and Data Science
Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.)