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- Faculty Publications (6)
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- All HBS Web (9)
- Faculty Publications (6)
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- 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.
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
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,... View Details
- 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).
Eliminating unintended bias in personalized policies using Bias Eliminating Adapted Trees (BEAT) - PNAS
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... View Details
- 01 Mar 2024
- News
Alumni and Faculty Books and Podcasts
Edited by Margie Kelley Alumni Books You Got This! A Straightforward, No-Nonsense Playbook for Crushing 130+ Workplace Challenges By Heidi Abelli (MBA 1993) Palmetto Publishing Stepping into the corporate world can feel like navigating a labyrinth, especially when... View Details
- Web
Technology & Operations Management Awards & Honors - Faculty & Research
Award at the INFORMS Workshop on Data Science for "Using Data-Mined Variables in Causal Inference Tasks: A Random Forest Approach to the Measurement Error Problem" with Mochen Yang, Gordon Burtch, and... View Details
- 08 Jan 2008
- First Look
First Look: January 8, 2008
an association, its structure presents many limitations and hurdles to overcome involving investing in technology platform updates and generating ideas and initiatives to monetize the use community. Purchase this case:... View Details
Keywords: Martha Lagace