Bayesian Causal Inference
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Foundations
1
Bayesian inference: optimisation and sampling
Preface
Foundations
Tools of Composition
1
Bayesian inference: optimisation and sampling
2
Causal quantities and g-computation
3
Causal thinking with graphs
4
Causal building blocks: forks, chains, and colliders
Causal inference with cross-sectional data
Composing Meaningful Models
5
Propensity Scores and Information Weighting
6
Supply, Demand and Instrumental Variables
7
Structural Equation Models and Path Diagrams
8
Discrete Choice Counterfactuals
9
Sensitivity Analysis: Model Checking and Theory Construction
Causal inference with panel data
Making the Counterfactual Explicit
10
Before and After: Change Scores to ANCOVA
11
Interrupted Time Series: No Control Group, Still Causal?
12
Synthetic Controls and Counterfactual Trajectories
13
Two-Way Fixed Effects: What Repeating Units Reveal
14
Marketing Mix Modelling and Experimental Calibration
Epilogue
References
Foundations
1
Bayesian inference: optimisation and sampling
1
Bayesian inference: optimisation and sampling
GO BUY THE BOOK!
Tools of Composition
2
Causal quantities and g-computation
