Clear, accessible explanations of new research, methods, and tools in causal inference and experimentation. We break down complex ideas without losing rigor.
New Methods & Techniques
Causal Inference with Multiple Simultaneous Treatments: Extending Double Machine Learning
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Bayesian Regression Discontinuity: Gaussian Process Priors and Flexible Inference at the Threshold
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The Borusyak-Jaravel-Spiess Imputation Estimator: Efficient DiD for Staggered Adoption Settings
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Regression Discontinuity with Multiple Running Variables: Frontier Designs and Identification
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Doubly Robust Difference-in-Differences: Sant'Anna and Zhao (2020)
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Dynamic Causal Effects Beyond Linearity: Local Projections in Nonlinear Settings
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The Generalised Propensity Score: Causal Inference with Continuous Treatments
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Local Projections: Jordà (2005) and the LP-IV Method for Dynamic Causal Effects
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Sensitivity Analysis for Observational Studies: Rosenbaum Bounds and Oster's Delta
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Partial Identification and Manski Bounds: How Much Can We Learn Without Strong Assumptions?
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Regression Kink Design: Identifying Causal Effects at Policy Kinks
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Recent Results: Education, Development, and Long-Run Outcomes (2022-2025)
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Honest DiD: Sensitivity Analysis for Pre-Trend Violations Rambachan and Roth (2023) and the HonestDiD Package
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Two-Way Fixed Effects with Heterogeneous Treatment Effects: The de Chaisemartin-D'Haultfoeuille (2020) Critique and the DID_M Estimator
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Regression Discontinuity Design: Sharp, Fuzzy, and the Calonico-Cattaneo-Titiunik Bandwidth
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Double Machine Learning: Debiased Estimation with High-Dimensional Controls
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