Filter Results:
(858)
Show Results For
- All HBS Web
(1,241)
- People (5)
- News (157)
- Research (858)
- Events (17)
- Multimedia (1)
- Faculty Publications (370)
Show Results For
- All HBS Web
(1,241)
- People (5)
- News (157)
- Research (858)
- Events (17)
- Multimedia (1)
- Faculty Publications (370)
Page 1 of 858
Results →
Sort by
- 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.
- 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.)
- 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.
- 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.
- 2015
- Working Paper
Selling to a Moving Target: Dynamic Marketing Effects in US Presidential Elections
By: Doug J. Chung and Lingling Zhang
We examine the effects of various political campaign activities on voter preferences in the domain of US Presidential elections. We construct a comprehensive data set that covers the three most recent elections, with detailed records of voter preferences at the... View Details
Keywords: Multi-channel Marketing; Personal Selling; Advertising; Political Campaigns; Dynamic Panel Data; Instrumental Variables; Marketing Communications; Political Elections; Advertising Campaigns; United States
Chung, Doug J., and Lingling Zhang. "Selling to a Moving Target: Dynamic Marketing Effects in US Presidential Elections." Harvard Business School Working Paper, No. 15-095, June 2015. (Revised December 2015.)
- September–October 2013
- Article
The Dynamic Advertising Effect of Collegiate Athletics
By: Doug J. Chung
I measure the spillover effect of intercollegiate athletics on the quantity and quality of applicants to institutions of higher education in the United States, popularly known as the "Flutie Effect." I treat athletic success as a stock of goodwill that decays over... View Details
Keywords: Choice Modeling; Entertainment Marketing; Heterogeneity; Panel Data; Structural Modeling; Rights; Analytics and Data Science; Higher Education; Ethics; Consumer Behavior; Advertising; Sports; Advertising Industry; Education Industry
Chung, Doug J. "The Dynamic Advertising Effect of Collegiate Athletics." Marketing Science 32, no. 5 (September–October 2013): 679–698. (Lead article. Featured in HBS Working Knowledge.)
- July 2006
- Article
Dynamic Mixed Duopoly: A Model Motivated by Linux vs. Windows
By: Ramon Casadesus-Masanell and Pankaj Ghemawat
This paper analyzes a dynamic mixed duopoly in which a profit-maximizing competitor interacts with a competitor that prices at zero (or marginal cost), with the cumulation of output affecting their relative positions over time. The modeling effort is motivated by... View Details
Keywords: Open Source Software; Demand-side Learning; Network Effects; Linux; Mixed Duopoly; Competitive Dynamics; Business Models; Duopoly and Oligopoly; Information Technology; Applications and Software; Business Model; Mathematical Methods; Digital Platforms; Profit; Balance and Stability; Management Analysis, Tools, and Techniques; SWOT Analysis; Competition; Price; Information Technology Industry
Casadesus-Masanell, Ramon, and Pankaj Ghemawat. "Dynamic Mixed Duopoly: A Model Motivated by Linux vs. Windows." Management Science 52, no. 7 (July 2006): 1072–1084.
- 08 Feb 2013
- Working Paper Summaries
The Dynamic Advertising Effect of Collegiate Athletics
- 22 Feb 2012
- Working Paper Summaries
The Dynamic Effects of Bundling as a Product Strategy
- November–December 2013
- Article
The Dynamic Effects of Bundling as a Product Strategy
By: Timothy Derdenger and Vineet Kumar
Several key questions in bundling have not been empirically examined: Is mixed bundling more effective than pure bundling or pure components? Does correlation in consumer valuations make bundling more or less effective? Does bundling serve as a complement or substitute... View Details
Derdenger, Timothy, and Vineet Kumar. "The Dynamic Effects of Bundling as a Product Strategy." Marketing Science 32, no. 6 (November–December 2013): 827–859.
- 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.
- 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.
- November 2019
- Article
How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove-Arrow Framework
By: Doug J. Chung, Byungyeon Kim and Byoung G. Park
This paper evaluates the short- and long-term value of sales representatives’ detailing visits to different types of physicians. By understanding the dynamic effect of sales calls across heterogeneous physicians, we provide guidance on the design of optimal call... View Details
Keywords: Nerlove-Arrow Framework; Stock-of-goodwill; Dynamic Panel Data; Serial Correlation; Instrumental Variables; Sales Effectiveness; Detailing; Analytics and Data Science; Sales; Analysis; Performance Effectiveness; Pharmaceutical Industry
Chung, Doug J., Byungyeon Kim, and Byoung G. Park. "How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove-Arrow Framework." Management Science 65, no. 11 (November 2019): 5197–5218.
- 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.
- 2021
- Working Paper
Population Interference in Panel Experiments
By: Iavor I Bojinov, Kevin Wu Han and Guillaume Basse
The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit's outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in... View Details
Bojinov, Iavor I., Kevin Wu Han, and Guillaume Basse. "Population Interference in Panel Experiments." Harvard Business School Working Paper, No. 21-100, March 2021.
- 2021
- Working Paper
The Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes
By: Arlen Guarin, Christian Posso, Estefania Saravia and Jorge Tamayo
Identifying the effect of physicians’ skills on health outcomes is a challenging task due to the nonrandom sorting between physicians and hospitals. We overcome this challenge by exploiting a Colombian government program that randomly assigned 2,126 physicians to 618... View Details
Keywords: Physicians' Health Skills; Health Birth Outcomes; Birthing Outcomes; Experimental Evidence; Health Care and Treatment; Competency and Skills; Outcome or Result; Health Industry; Colombia
Guarin, Arlen, Christian Posso, Estefania Saravia, and Jorge Tamayo. "The Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes." Harvard Business School Working Paper, No. 22-015, February 2021. (R&R American Economic Journal.)
- 2015
- Chapter
How Leaders Use Values-based Guidance Systems to Create Dynamic Capabilities
How do strategic leaders create change-adept organizations? Based on qualitative field research, this chapter argues that well-defined institutionalized purpose, values, and principles act as an organizational guidance system that integrates and strengthens the... View Details
Keywords: Dynamic Capabilities; Field Research; Intrinsic Motivation; Organizational Identity; Ecosystem; Organizational Change and Adaptation; Mission and Purpose; Motivation and Incentives; Research; Management Systems; Change
Kanter, Rosabeth M., Matthew Bird, Ethan Bernstein, and Ryan Raffaelli. "How Leaders Use Values-based Guidance Systems to Create Dynamic Capabilities." Chap. 2 in The Oxford Handbook of Dynamic Capabilities, edited by David J. Teece and Sohvi Leih. Oxford University Press, 2015. Electronic.
- 2021
- Article
The Virtues and Limitations of Dynamic Capabilities
By: Bharat Anand and David J. Collis
Dynamic capabilities have been identified as a key determinant of competitive advantage. This paper explores the foundations of dynamic capabilities, and the limits to their effectiveness, first theoretically and then through the case of Danaher, the most successful... View Details
Keywords: Dynamic Capabilities; Danaher; Resources; Theory Of The Firm; Value-based Strategy; Organizations; Performance Effectiveness; Competitive Advantage; Strategy
Anand, Bharat, and David J. Collis. "The Virtues and Limitations of Dynamic Capabilities." Strategic Management Review 2, no. 1 (2021): 47–78.
- March 2021
- Article
The Crowd Emotion Amplification Effect
By: Amit Goldenberg, Erika Weisz, Timothy D. Sweeney, Mina Cikara and James Gross
How do people go about reading a room or taking the temperature of a crowd? When people catch a brief glimpse of an array of faces, they can only focus their attention on some of the faces. We propose that perceivers preferentially attend to faces exhibiting strong... View Details
Goldenberg, Amit, Erika Weisz, Timothy D. Sweeney, Mina Cikara, and James Gross. "The Crowd Emotion Amplification Effect." Psychological Science 32, no. 3 (March 2021): 437–450.
- 2025
- Working Paper
Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning
Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships
between customer states, company actions, and long-term value. However, its practical implementation often faces significant
challenges.... View Details
Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science
Ma, Liangzong, Ta-Wei Huang, Eva Ascarza, and Ayelet Israeli. "Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning." Harvard Business School Working Paper, No. 25-037, February 2025.