Modern Data Analysis for Economics: Foundations of Causal Inference (Part III)

Foundations of Causal Inference

Topics: causal effect and causal mechanism learning; identification and estimation; discriminative vs. generative causal inference; Rubin causal model; potential outcomes framework; average treatment effect; randomized controlled trials (RCT); exchangeability; self-selection bias; sample-selection bias; internal and external validity; SUTVA; Bayesian belief network; causal graphical model; endogeneity and exogeneity; do-calculus; reduced-form analysis; back-door and front-door criterion; structural estimation; dynamic structural model; counterfactual simulation

2019 Fall @ Xiamen University

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Modern Data Analysis for Economics

This course offers a unified introduction to the principles and methods of statistical modeling and causal inference – two areas essential to data analysis in economics. The first part of this course introduces learning theory and a number of modern machine learning methods used for pattern recognition and predictive modeling. The second part introduces the theory of causal inference and surveys frequently used econometric techniques for causal effect learning and program evaluation. Finally, we discuss structural estimation and offer a unified perspective on the use of reduced-form and structural econometric methods.

Course website: https://jiamingmao.github.io/data-analysis/

Видео Modern Data Analysis for Economics: Foundations of Causal Inference (Part III) канала Jiaming Mao