Global Certificate in Causal Effectiveness Approaches
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โข Causal Inference: Understanding the fundamentals of causal inference, including causal models, causal diagrams, and potential outcomes.
โข Experimental Design: Designing and implementing randomized experiments, A/B testing, and factorial designs.
โข Observational Studies: Analyzing observational data, including confounding, selection bias, and measurement error.
โข Propensity Score Matching: Matching techniques, propensity score estimation, and evaluation of balance.
โข Regression Analysis: Linear and logistic regression for causal inference, including models for continuous and binary outcomes.
โข Difference-in-Differences: Estimation and interpretation of difference-in-differences models, including parallel trends and common trends assumptions.
โข Instrumental Variables: Identification, estimation, and interpretation of instrumental variables, including two-stage least squares and limited information maximum likelihood.
โข Quasi-Experimental Designs: Regression discontinuity, interrupted time series, and synthetic control methods.
โข Sensitivity Analysis: Quantifying the robustness of causal estimates to assumptions, including placebo tests and E-values.
โข Causal Machine Learning: Machine learning algorithms for causal inference, including Bayesian additive regression trees, causal forests, and double/debiased machine learning.
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