Beginner's Corner

A welcoming entry point for readers new to causal inference. We explain core concepts such as randomized experiments, counterfactuals, instrumental variables, difference-in-differences, and causal graphs in plain language.

Conjoint Analysis and Survey Experiments: Measuring Causal Preferences

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Interrupted Time Series: A Beginner's Guide to Causal Inference with Time-Series Data

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The Regression Anatomy Formula: What Multiple Regression Really Estimates

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What Does "Identification" Mean? A Beginner's Guide to Causal Identification

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Good Controls and Bad Controls: When Does Adding a Variable Hurt?

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The Synthetic Control Method: A Beginner's Guide to Building Counterfactuals

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Power Calculations for Causal Studies: How Many Observations Do You Need?

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Fixed Effects and Panel Data: Controlling for What You Cannot Observe

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Randomization Inference: Fisher's Exact Test and Permutation P-Values

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Event Studies from Scratch: How to Plot and Interpret Treatment Dynamics

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Selection Bias and Why OLS Can Lie: The Omitted Variable Problem

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Why Randomise? The Logic and Power of Randomised Controlled Trials

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Directed Acyclic Graphs: Drawing Your Causal Assumptions

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Matching and Propensity Scores: Balancing Groups Without Randomisation

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Regression Discontinuity: When a Threshold Becomes an Experiment

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Difference-in-Differences from Scratch

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What Is a Counterfactual?

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Instrumental Variables from Scratch: The LATE Theorem and Complier Analysis

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Beginner's Corner

A welcoming entry point for readers new to causal inference. We explain core concepts such as randomized experiments, counterfactuals, instrumental variables, difference-in-differences, and causal graphs in plain language.

The most fundamental concept in causal inference explained without equations and why it matters for every decision.

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The most repeated phrase in statistics finally gets the explanation it deserves, with real examples and practical guidance.

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Directed acyclic graphs made simple. How to draw a causal story and use it to figure out what to control for.

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