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Python으로 하는 인과추론

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01 - Introduction To Causality
02 - Randomised Experiments
03 - Stats Review: The Most Dangerous Equation
04 - Graphical Causal Models
05 - The Unreasonable Effectiveness of Linear Regression

06 - Grouped and Dummy Regression

07 - Beyond Confounders

08 - Instrumental Variables

09 - Non Compliance and LATE

11 - Propensity Score

12 - Doubly Robust Estimation

13 - Difference-in-Differences

14 - Panel Data and Fixed Effects

15 - Synthetic Control

16 - Regression Discontinuity Design

17 - Predictive Models 101

18 - Hatarogeneous Treatment Effects and Personalization

Site Navigation

01 - Introduction To Causality
02 - Randomised Experiments
03 - Stats Review: The Most Dangerous Equation
04 - Graphical Causal Models
05 - The Unreasonable Effectiveness of Linear Regression

06 - Grouped and Dummy Regression

07 - Beyond Confounders

08 - Instrumental Variables

09 - Non Compliance and LATE

11 - Propensity Score

12 - Doubly Robust Estimation

13 - Difference-in-Differences

14 - Panel Data and Fixed Effects

15 - Synthetic Control

16 - Regression Discontinuity Design

17 - Predictive Models 101

18 - Hatarogeneous Treatment Effects and Personalization

Python으로 하는 인과추론

Ctrl+K

Causal Inference for The Brave and True

인과추론 Part I

01 - Introduction To Causality
02 - Randomised Experiments
03 - Stats Review: The Most Dangerous Equation
04 - Graphical Causal Models
05 - The Unreasonable Effectiveness of Linear Regression
06 - Grouped and Dummy Regression
07 - Beyond Confounders
08 - Instrumental Variables
09 - Non Compliance and LATE
10 - Matching
11 - Propensity Score
12 - Doubly Robust Estimation
13 - Difference-in-Differences
14 - Panel Data and Fixed Effects
15 - Synthetic Control
16 - Regression Discontinuity Design

인과추론 Part II

17 - Predictive Models 101
18 - Hatarogeneous Treatment Effects and Personalization
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By Jinsoo Shin

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