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Course: A Crash Course in Causality: Inferring Causal Effects from Observational Data. Click here to go back.

- Welcome to "A Crash Course in Causality"
- Confusion over causality
- Potential outcomes and counterfactuals
- Hypothetical interventions
- Causal effects
- Causal assumptions
- Stratification
- Incident user and active comparator designs
- Confounding
- Causal graphs
- Relationship between DAGs and probability distributions
- Paths and associations
- Conditional independence (d-separation)
- Confounding revisited
- Backdoor path criterion
- Disjunctive cause criterion
- Observational studies
- Overview of matching
- Matching directly on confounders
- Greedy (nearest-neighbor) matching
- Optimal matching
- Assessing balance
- Analyzing data after matching

- Sensitivity analysis
- Data example in R
- Propensity scores
- Propensity score matching
- Propensity score matching in R
- Intuition for Inverse Probability of Treatment Weighting (IPTW)
- More intuition for IPTW estimation
- Marginal structural models
- IPTW estimation
- Assessing balance
- Distribution of weights
- Remedies for large weights
- Doubly robust estimators
- Data example in R
- Introduction to instrumental variables
- Randomized trials with noncompliance
- Compliance classes
- Assumptions
- Causal effect identification and estimation
- IVs in observational studies
- Two stage least squares
- Weak instruments
- IV analysis in R