Other Causal Inference Tools

In a world full of complex data, organizations can leverage causal inference tools to generate more accurate and precise insights, leading to better decisions and more effective outcomes.

Our lab uses causal inference methods and tools such as staggered rollout, federated learning, and surrogates to analyze large volumes of data, identify trade offs, and optimize experimental design to maximize the impact of interventions.

Project Abstracts

Read about some of the research projects the lab is working on.

Academic Publications

Publication Search
Journal Article

Federated Causal Inference in Heterogeneous Observational Data

Ruoxuan Xiong, Allison Koenecke, Michael Powell, Zhu Shen, Joshua T. Vogelstein, Susan Athey
Statistics in Medicine August2023
Journal Article

Stable Learning Establishes Some Common Ground between Causal Inference and Machine Learning

Peng Cui, Susan Athey
Nature Machine Intelligence February2022

Academic Presentations

Watch talks given by the lab’s researchers.

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Tools & Tutorials

Explore and apply the tools and tutorials created by the lab.

Ten Rules for Conducting Retrospective Pharmacoepidemiological Analyses: Example COVID-19 Study

This guide presents 10 rules that serve as an end-to-end introduction to retrospective pharmacoepidemiological analyses of observational health care data using a running example of a hypothetical COVID-19 study. When carefully designed and properly executed, a retrospective pharmacoepidemiological analysis framed around these rules will inform the decisions of whether and how to investigate a treatment hypothesis in a randomized controlled trial.

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