1 - A Brief Introduction to Causal Inference (Course Preview)
We give you a taste of what we'll cover in the first few weeks of the Introduction to Causal Inference online course. Please post questions in the YouTube comments section.
Introduction to Causal Inference Course Website: causalcourse.com
0:00 What to expect
1:02 What is causal inference?
2:17 Talk outline
3:00 Motivating example: Simpson's paradox
14:43 Correlation does not imply causation
21:58 Then, what does imply causation?
30:28 Causation in observational studies
Видео 1 - A Brief Introduction to Causal Inference (Course Preview) канала Brady Neal - Causal Inference
Introduction to Causal Inference Course Website: causalcourse.com
0:00 What to expect
1:02 What is causal inference?
2:17 Talk outline
3:00 Motivating example: Simpson's paradox
14:43 Correlation does not imply causation
21:58 Then, what does imply causation?
30:28 Causation in observational studies
Видео 1 - A Brief Introduction to Causal Inference (Course Preview) канала Brady Neal - Causal Inference
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24 августа 2020 г. 6:18:03
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Другие видео канала
3.6 - Chains and Forks
5.3 - The Frontdoor Adjustment
11.6 - Parametric Interventions for Causal Discovery
7.6 - More Flexible Sensitivity Analysis
9.4 - Problems with Difference-in-Differences
12.3 - Transportability of Causal Effects Across Populations
9.1 - Difference-in-Differences Motivation and Preliminaries
3.4 - Causal Graphs
9.3 - Difference-in-Differences Assumptions and Proof
6.1 - CATE Preliminaries and Outline
10.1 - Causal Discovery Motivation and Outline
10.7 - Nonlinear Additive Noise Setting for Causal Discovery
2.10 - Estimands, Estimates, and the Identification-Estimation Flowchart
12.2 - Causal Insights for Transfer Learning
3.9 - The Flow of Association and Causation in Graphs
4.9 - M-Bias and Conditioning on Descendants of Treatment
10.5 - Impossibility Results for Independence-Based and Nonparametric Causal Discovery
4.10 - A Complete Graphical Example with Estimation
14.3 - Mediation
10.6 - Linear Non-Gaussian Setting for Causal Discovery
12.1 - Transfer Learning, Domain Generalization, and Covariate Shift