Causal Inference with Modern Machine Learning Methods
Doctoral-level introduction to causal inference at the intersection of machine learning, focusing on theoretical foundations and recent research developments.
Course Description
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In contemporary causal inference studies, the data are decidedly large-scale, complex, and high-dimensional.
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Recently, an exciting set of tools at the intersection of causal inference and machine learning has emerged to tackle these types of questions in these settings.
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The first 2.5 weeks is spent on Rubin’s causl framework and causal ifernce basics at the level of Rubin 2015 book.
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Then we study recent developments for causal inference using modern machine learning methods. The content will primarily be based on recent research papers.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Explain the core concepts and challenges in causal inference under the potential outcomes framework;
- Apply the most recent developments of modern statistical/machine learning methods to some core causal inference problems;
- Demonstrate and improve the ability to develop and justify the statistical/machine learning methods with mathematical rigor when applying/adapting them to causal inference questions.
Related Posts
Project Updates
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Causal Inference Overview
Understanding SUTVA and ignorability, fundamental assumptions in causal inference, what they mean, and what happens when they are violated.
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Causal Assumption: SUTVA and Ignorability
Understanding SUTVA and ignorability, fundamental assumptions in causal inference, what they mean, and what happens when they are violated.
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Causal Inference vs Causal Estimation
A deep dive into confidence intervals—what they are, how to interpret them, some caveats, and oft-encountered issues in online experimentation.
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Violation of SUTVA in A/B Testing: network interference
A summary of the Lyft Engineering blog post 'Interference Across a Network' detailing how naive A/B testing can bias effect estimates in ridesharing.
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Switchback Experiments
An overview of switchback (time-split) experiments: what they are, why they are used to solve network interference, and their trade-offs.
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Synthetic Control and Experimentation Culture
When standard experiments fail: utilizing Synthetic Controls, managing experimentation culture, and understanding various treatment effects.
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Case Study: Causal Effect of ETA Reduction
A practical case study on measuring the causal effect of reducing Estimated Time of Arrival (ETA) in a ridesharing marketplace.
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Case Study: Causal Effect of ETA Reduction
A practical case study on measuring the causal effect of reducing Estimated Time of Arrival (ETA) in a ridesharing marketplace.
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Paper review: Statistical Challenges in Online Controlled Experiments
A review of Larsen et al. (2024) on the statistical landscape and challenges of A/B testing in large-scale online environments.
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Case Study: Diagnosing and Addressing a Metric Drop
An end-to-end framework for investigating MAU drops, targeting at-risk users, and making data-driven 'ship' decisions.
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Quick and Dirty Sample Size Calculation
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A/B Testing Metrics
A comprehensive guide to selecting and evaluating metrics in A/B testing and online experimentation.
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Summary: Challenges in Experimentation (Lyft)
A summary of the Lyft Engineering blog post 'Challenges in Experimentation' by John Kirn.
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Paper review: Performance Guarantees for Individualized Treatment Rules
A review of Qian and Murphy (2011) on formulating individualized treatment rules via conditional outcome maximization with performance guarantees.
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Paper review: Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
A review of Scharfstein, Rotnitzky, and Robins (1999) on handling nonignorable missing data and conducting sensitivity analysis using semiparametric methods.
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Paper review: A Distributional Approach for Causal Inference Using Propensity Scores
A review of Tan (2006) on estimating average causal effects and improving upon propensity score methods using a distributional approach.
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Paper review: Sensitivity Analysis for Inverse Probability Weighting Estimators via the Percentile Bootstrap
A review of Zhao, Small, and Bhattacharya (2019) on conducting robust sensitivity analysis for IPW estimators using marginal sensitivity models and the percentile bootstrap.
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Paper review: Data Fusion for High-Resolution Estimation
A review of Guan et al. (2026) on fusing unbiased administrative data with biased survey data for high-resolution estimation.