Causal Machine Learning for Population Segment Discovery and Analysis
Authors: Nima Hejazi and Wenjing Zheng
Causal Segmentation Analysis with sherlock
The sherlock R package implements an approach for population segmentation analysis (or subgroup discovery) using recently developed techniques from causal machine learning. Using data from randomized A/B experiments or observational studies (quasi-experiments), sherlock takes as input a set of user-selected candidate segment dimensions – often, a subset of measured pre-treatment covariates – to discover particular segments of the study population based on the estimated heterogeneity of their response to the treatment under consideration. In order to quantify this treatment response heterogeneity, the conditional average treatment effect (CATE) is estimated using a nonparametric, doubly robust framework (Vanderweele et al. 2019; van der Laan and Luedtke 2015; Luedtke and van der Laan 2016b, 2016a), incorporating state-of-the-art ensemble machine learning (van der Laan, Polley, and Hubbard 2007; Coyle et al. 2021) in the estimation procedure.
For background and details on using sherlock, see the package vignette and the documentation site. An overview of the statistical methodology is available in our conference manuscript (Hejazi, Zheng, and Anand 2021) from CODE @ MIT 2021.
Installation
Install the most recent version from the master branch on GitHub via remotes:
remotes::install_github("Netflix/sherlock")Citation
After using the sherlock R package, please cite the following:
@software{netflix2021sherlock,
author={Hejazi, Nima S and Zheng, Wenjing and {Netflix, Inc.}},
title = {{sherlock}: Causal machine learning for segment discovery
and analysis},
year = {2021},
note = {R package version 0.2.0},
doi = {10.5281/zenodo.5652010},
url = {https://github.com/Netflix/sherlock}
}
@article{hejazi2021framework,
author = {Hejazi, Nima S and Zheng, Wenjing and Anand, Sathya},
title = {A framework for causal segmentation analysis with machine
learning in large-scale digital experiments},
year = {2021},
journal = {Conference on Digital Experimentation at {MIT}},
volume = {(8\textsuperscript{th} annual)},
publisher = {MIT Press},
url = {https://arxiv.org/abs/2111.01223}
}License
The contents of this repository are distributed under the Apache 2.0 license. See file LICENSE.md for details.
References
Coyle, Jeremy R, Nima S Hejazi, Ivana Malenica, Rachael V Phillips, and Oleg Sofrygin. 2021. sl3: Modern Pipelines for Machine Learning and Super Learning. https://doi.org/10.5281/zenodo.1342293.
Hejazi, Nima S, Wenjing Zheng, and Sathya Anand. 2021. “A Framework for Causal Segmentation Analysis with Machine Learning in Large-Scale Digital Experiments.” Conference on Digital Experimentation at MIT (8th annual). https://arxiv.org/abs/2111.01223.
Luedtke, Alex, and Mark van der Laan. 2016a. “Optimal Individualized Treatments in Resource-Limited Settings.” International Journal of Biostatistics 12 (1): 283–303. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6052884/.
———. 2016b. “Super-Learning of an Optimal Dynamic Treatment Rule.” International Journal of Biostatistics 12 (1): 305–32. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6056197/.
van der Laan, Mark J, Eric C Polley, and Alan E Hubbard. 2007. “Super Learner.” Statistical Applications in Genetics and Molecular Biology 6 (1).
van der Laan, Mark, and Alex Luedtke. 2015. “Targeted Learning of the Mean Outcome Under an Optimal Dynamic Treatment Rule.” Journal of Causal Inference 3 (1): 61–95. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4517487/.
Vanderweele, Tyler, Alex Luedtke, Mark van der Laan, and Ronald Kessler.
- “Selecting Optimal Subgroups for Treatment Using Many Covariates.” Epidemiology 30 (3): 334–41. https://journals.lww.com/epidem/Fulltext/2019/05000/Selecting_Optimal_Subgroups_for_Treatment_Using.5.aspx.
