"Introduction to Causal Inference" by Maya Petersen

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Introduction to Causal Inference

(2014)

Maya Petersen, University of California, Berkeley
Laura Balzer

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Abstract

This course presents a general framework for causal inference. Directed acyclic graphs and non-parametric structural equation models (NPSEM) are used to define the causal model. Target causal parameters are defined using counterfactuals and marginal structural models. G-computation estimators, inverse probability weighted estimators, and targeted maximum likelihood estimators are introduced. Non-parametric and semi-parametric approaches to nuisance parameter estimation, with an emphasis on Super Learning, are presented. Students gain practical experience implementing these estimators and interpreting results through discussion assignments, R labs, and R assignments.

Keywords

causal inference,
TMLE

Disciplines

Public Health

Publication Date

Summer 2014

Citation Information

Maya Petersen and Laura Balzer. "Introduction to Causal Inference" (2014)
Available at: http://works.bepress.com/laura_balzer/37/