8.10.5 Genetic Matching: Single variable.8.5 Exercise: Logic of & varieties of matching.7.6 Pervasive problem: Example post-treatment bias.7.5 Covariates: Endogenous selection bias.

7.4 Covariates: Confounding/overcontrol bias.

6.8 Natural experiments: Challenges & Criticism.

6.3 Ideal experiments: Possible “ideal” design.

5.4.1 Long-run randomization & balance (not finished).

5.4 Lab: How randomization induces independence.

4.34 Identification Analysis & Strategy.

4.33 Causes: Manipulable causes (Discussion).

4.32 Causes: No causation without manipulation.

4.31 Causes: Which variables are causes? (Discussion).

4.29 Exercise: Treatments/outcome as trajectories.

4.27 ATT: Average Effect of the Treatment on the Treated and the Control.

4.23 Independence assumption & random assignment.

4.22 Assumptions: Independence Assumption (IA).

4.21 Assumptions: Independence Assumption (IA).

4.16 Why moving from ITE to ATE? (Wikipedia).

4.10 Definition of Treatment/Causal Effect.

4.9 Potential outcomes (multiple treatment values) (skip!).

4.5 Causal chains & causal mechanism (3).

4.4 Causal chains & causal mechanism (2).

4.3 Causal chains & causal mechanism (1).

4.2 Deterministic vs. probabilistic causation.

4 Causal Analysis: Concepts & Definitions.

3.22 Models: Associational vs. causal inference.

3.21 Models: Estimand, estimator and estimation (skip).

3.18 Models: Example: Linear model (Visualization).

3.17 Models: Example: Linear model (Equation).

3.13 Data: Probability Distributions & Inference.

3.10 Data: (Empirical) Joint distributions.

3.9 Data: (Empirical) Univariate distributions.3.5 Measurement: Scenarios, planned and realized measurements.3.4 Measurement: Distribution(s) of measurements.3 Introduction: Fundamental statistical concepts.1.4 Motivation: The causal inference ‘revolution’.1 Introduction: About this seminar/book.

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