Feature Stories

In-depth explorations of the most important ideas, debates, and breakthroughs in causal inference. Feature stories connect theory to practice, covering causal AI, experimentation at scale, policy evaluation, and the future of evidence-based decision-making.

Matrix Completion for Panel Data: Athey et al. (2021) and the Next Generation of Counterfactual Methods

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A Tale of Three Frameworks: Reconciling Potential Outcomes, Structural Equations, and Directed Acyclic Graphs

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Difference-in-Differences Meets Synthetic Control: The New Wave of Doubly Robust Hybrid Methods

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Peer Effects and the Reflection Problem: Identification in Social Interactions

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Causal Inference with Administrative Data: Opportunities and Pitfalls

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Shift-Share Instruments: Design-Based vs Model-Based Identification

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LLMs and Causal Discovery: Can Large Language Models Identify Causal Structure?

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Beyond Binary: Difference-in-Differences with a Continuous Treatment

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The Rise of Causal Inference in Economics: A Meta-Science Perspective

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Marginal Treatment Effects: The Bridge Between LATE, Selection Models, and Policy Analysis

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Bunching Estimators: Identifying Behavioural Responses at Kinks and Notches

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Interference and Spillovers: When SUTVA Fails and What To Do About It

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The Synthetic Control Method: Building Counterfactuals from Data

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The Goodman-Bacon Decomposition: What Two-Way Fixed Effects Actually Estimates in Staggered Adoption Settings

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Instrumental Variables: The Most Powerful and Most Abused-Tool in Econometrics

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Natural Experiments: Finding Causal Evidence Without Randomisation

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Heterogeneous Treatment Effects: Beyond the Average

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The Credibility Revolution in Econometrics: Thirty Years On

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From Phillip Wright’s tariff problem to shift-share designs, IV has shaped a century of causal inference—and generated as much controversy as insight. A deep history and critical guide.

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Without a randomised trial, can we still know what causes what? Natural experiments—from draft lotteries to monsoon rainfall—show how the right accident of history can answer the hardest causal questions.

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