Filter Results
:
(92)
Show Results For
-
All HBS Web
(364)
- Faculty Publications (92)
Show Results For
-
All HBS Web
(364)
- Faculty Publications (92)
- December 2022
- Article
Social Skills Improve Business Performance: Evidence from a Randomized Control Trial with Entrepreneurs in Togo
By: Stefan Dimitriadis and Rembrand Koning
Recent field experiments demonstrate that advice, mentorship, and feedback from randomly assigned peers improve entrepreneurial performance. These results raise a natural question: what is preventing entrepreneurs and managers from forming these peer connections...
View Details
Keywords:
Social Skills;
Business Performance;
Entrepreneurs;
Peer Relationships;
Field Experiment;
Entrepreneurship;
Performance;
Relationships;
Interpersonal Communication;
Togo
Dimitriadis, Stefan, and Rembrand Koning. "Social Skills Improve Business Performance: Evidence from a Randomized Control Trial with Entrepreneurs in Togo." Management Science 68, no. 12 (December 2022): 8635–8657.
- 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.
- 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).
- June 2022 (Revised July 2022)
- 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 July 2022.)
- 2022
- Working Paper
Is Hybrid Work the Best of Both Worlds? Evidence from a Field Experiment
Hybrid work is emerging as a novel form of organizing work globally. This paper reports causal evidence on how the extent of hybrid work—the number of days worked from home relative to days worked from the office—affects work outcomes. Collaborating with an...
View Details
Keywords:
Hybrid Work;
Remote Work;
Work-from-home;
Field Experiment;
Employees;
Geographic Location;
Performance;
Work-Life Balance
Choudhury, Prithwiraj, Tarun Khanna, Christos A. Makridis, and Kyle Schirmann. "Is Hybrid Work the Best of Both Worlds? Evidence from a Field Experiment." Harvard Business School Working Paper, No. 22-063, March 2022.
- 2023
- Working Paper
Can Evidence-Based Information Shift Preferences Towards Trade Policy?
By: Laura Alfaro, Maggie X. Chen and Davin Chor
We investigate the role of evidence-based information in shaping individuals' preferences for trade policies through a series of survey experiments that contain randomized information treatments. Each treatment provides a concise statement of economics research...
View Details
Alfaro, Laura, Maggie X. Chen, and Davin Chor. "Can Evidence-Based Information Shift Preferences Towards Trade Policy?" Harvard Business School Working Paper, No. 22-062, March 2022. (Revised May 2023. NBER Working Paper Series, No. 31240, May 2023)
- 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.
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
Keywords:
Algorithm Bias;
Personalization;
Targeting;
Generalized Random Forests (GRF);
Discrimination;
Customization and Personalization;
Decision Making;
Fairness;
Mathematical Methods
Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 2022).
- Article
Corruption and Firms
By: Emanuele Colonnelli and Mounu Prem
We estimate the causal real economic effects of a randomized anti-corruption crackdown on local governments in Brazil using rich micro-data on corruption and firms. After anti-corruption audits, municipalities experience an increase in the number of firms concentrated...
View Details
Colonnelli, Emanuele, and Mounu Prem. "Corruption and Firms." Review of Economic Studies 89, no. 2 (March 2022): 695–732.
- March 2022
- Article
Sensitivity Analysis of Agent-based Models: A New Protocol
By: Emanuele Borgonovo, Marco Pangallo, Jan Rivkin, Leonardo Rizzo and Nicolaj Siggelkow
Agent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such...
View Details
Keywords:
Agent-based Modeling;
Sensitivity Analysis;
Design Of Experiments;
Total Order Sensitivity Indices;
Organizations;
Behavior;
Decision Making;
Mathematical Methods
Borgonovo, Emanuele, Marco Pangallo, Jan Rivkin, Leonardo Rizzo, and Nicolaj Siggelkow. "Sensitivity Analysis of Agent-based Models: A New Protocol." Computational and Mathematical Organization Theory 28, no. 1 (March 2022): 52–94.
- 2022
- Working Paper
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." Working Paper, March 2022.
- February 15, 2022
- Article
How Managers Can Build a Culture of Experimentation
By: Frank V. Cespedes and Neil Hoyne
Testing in business presents qualitatively different challenges than those in clinical trials and most scientific research. There are very few opportunities for randomized control experiments in a changing, competitive market. Yet, change and competition make testing a...
View Details
Cespedes, Frank V., and Neil Hoyne. "How Managers Can Build a Culture of Experimentation." Harvard Business Review Digital Articles (February 15, 2022).
- Article
A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects
By: Edward McFowland III, Sandeep Gangarapu, Ravi Bapna and Tianshu Sun
We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility maximizing context, public...
View Details
Keywords:
Prescriptive Analytics;
Heterogeneous Treatment Effects;
Optimization;
Observed Rank Utility Condition (OUR);
Between-treatment Heterogeneity;
Machine Learning;
Decision Making;
Analysis;
Mathematical Methods
McFowland III, Edward, Sandeep Gangarapu, Ravi Bapna, and Tianshu Sun. "A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects." MIS Quarterly 45, no. 4 (December 2021): 1807–1832.
- 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.
- Article
Using Fresh Starts to Nudge Increased Retirement Savings
By: John Beshears, Hengchen Dai, Katherine L. Milkman and Shlomo Benartzi
We conducted a field experiment to study the effect of framing future moments in time as new beginnings (or “fresh starts”). University employees (N=6,082) received mailings with an opportunity to choose between increasing their contributions to a savings plan...
View Details
Keywords:
Choice Architecture;
Randomized Field Experiment;
Savings;
New Beginning;
Fresh Start;
Saving;
Retirement;
Behavior
Beshears, John, Hengchen Dai, Katherine L. Milkman, and Shlomo Benartzi. "Using Fresh Starts to Nudge Increased Retirement Savings." Organizational Behavior and Human Decision Processes 167 (November 2021): 72–87.
- October 2021
- Article
Changing Gambling Behavior through Experiential Learning
By: Shawn A. Cole, Martin Abel and Bilal Zia
This paper tests experiential learning as a debiasing tool to reduce gambling in South Africa, through a randomized field experiment. The study implements a simple, interactive game that simulates the odds of winning the national lottery through dice rolling....
View Details
Keywords:
Debiasing;
Experiential Learning;
Behavioral Economics;
Financial Education;
Learning;
Games, Gaming, and Gambling;
Behavior;
Decision Making
Cole, Shawn A., Martin Abel, and Bilal Zia. "Changing Gambling Behavior through Experiential Learning." World Bank Economic Review 35, no. 3 (October 2021): 745–763.
- Article
Measuring the Scientific Effectiveness of Contact Tracing: Evidence from a Natural Experiment
By: Thiemo Fetzer and Thomas Graeber
Contact tracing has for decades been a cornerstone of the public health approach to epidemics, including Ebola, severe acute respiratory syndrome, and now COVID-19. It has not yet been possible, however, to causally assess the method’s effectiveness using a randomized...
View Details
Fetzer, Thiemo, and Thomas Graeber. "Measuring the Scientific Effectiveness of Contact Tracing: Evidence from a Natural Experiment." Proceedings of the National Academy of Sciences 118, no. 33 (August 17, 2021): 1–4.
- 2023
- Working Paper
Should Workplace Programs Be Voluntary or Mandatory? Evidence from a Field Experiment on Mentorship
By: Jason Sandvik, Richard Saouma, Nathan Seegert and Christopher Stanton
In a field experiment, we find large differences in productivity treatment effects between voluntary and mandatory workplace mentorship programs. A significant portion of this difference is due to the best employees opting into the program when it is voluntary and...
View Details
Keywords:
Mentoring;
Mentorship Programs;
Randomized Controlled Trial;
Employees;
Relationships;
Programs;
Performance
Sandvik, Jason, Richard Saouma, Nathan Seegert, and Christopher Stanton. "Should Workplace Programs Be Voluntary or Mandatory? Evidence from a Field Experiment on Mentorship." NBER Working Paper Series, No. 29148, August 2021. (Revised October 2023.)
- July 2021
- Article
Invisible Inequality Leads to Punishing the Poor and Rewarding the Rich
By: Oliver P. Hauser, Gordon T. Kraft-Todd, David Rand, Martin A. Nowak and Michael I. Norton
Four experiments examine how the lack of awareness of inequality affects behaviour towards the rich and poor. In Experiment 1, participants who became aware that wealthy individuals donated a smaller percentage of their income switched from rewarding the wealthy to...
View Details
Keywords:
Income Transparency;
Income;
Wealth;
Equality and Inequality;
Knowledge;
Behavior;
Outcome or Result;
Society;
Policy
Hauser, Oliver P., Gordon T. Kraft-Todd, David Rand, Martin A. Nowak, and Michael I. Norton. "Invisible Inequality Leads to Punishing the Poor and Rewarding the Rich." Behavioural Public Policy 5, no. 3 (July 2021): 333–353.
- 2023
- Working Paper
Virtual Water Coolers: A Field Experiment on the Role of Virtual Interactions on Organizational Newcomer Performance
Designing management practices to better onboard organizational newcomers working remotely is a key priority for firms. We report results from a randomized field experiment conducted at a large global firm that estimates the performance effects of different types of...
View Details
Keywords:
Remote Work;
Virtual Water Coolers;
Social Interactions;
Careers;
Field Experiment;
Employees;
Interpersonal Communication;
Internet and the Web;
Performance;
Personal Development and Career
Choudhury, Prithwiraj, Jacqueline N. Lane, and Iavor Bojinov. "Virtual Water Coolers: A Field Experiment on the Role of Virtual Interactions on Organizational Newcomer Performance." Harvard Business School Working Paper, No. 21-125, May 2021. (Revised February 2023.)