Topics in causal inference-清华大学求真书院

短期课程

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Topics in causal inference

来源： 06-05

时间：Mon.& Wed., 19:20–20:55 June 5-July 31, 2023

地点：Lecture Hall, 3rd Floor Jin Chun Yuan West Bldg.

主讲人：Peng Ding 丁鹏 University of California, Berkeley

Speaker

Peng Ding is an Associate Professor in the Department of Statistics, UC Berkeley, working on causal inference. He obtained his Ph.D. from the Department of Statistics and worked as a postdoctoral researcher in the Department of Epidemiology, both at Harvard.

Course Description

This course will cover the following basic topics:

- randomization inference in experiments: design and analysis

- observational studies: identification and estimation with and without unconfoundedness, sensitivity analysis

- causal mechanisms: post-treatment complications, interaction

- difference in differences and panel data

- spillover and peer effects

Prerequisite

Calculus, linear algebra, probability, statistics

Reference

Lecture notes from the instructor

Target Audience

Undergraduate students & Graduate students

Teaching Language

Chinese

返回顶部

Recent advances in causal inference
Description:I will give four lectures based on the following papers.Gao, M. and Ding. P. (2023+). Causal inference in network experiments: regression-based analysis and design-based properties.Lu, S. and Ding, P. (2023+). Flexible sensitivity analysis for causal inference in observational studies subject to unmeasured confounding.Lu, S., Jiang, Z. and Ding, P. (2023+) Principal Stratification w...
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Matched Sampling for Causal Inference
Description: Matched sampling, where the researcher first finds for each exposed unit (e.g., a smoker) a non-exposed unit (e.g., a never-smoker) who looks exactly like the exposed unit except for the exposure, and then compares outcomes (e.g., lung cancer rates) for the matched samples of units, is an intuitive method for inferring the causal effects of exposure versus non-exposure on the colle...
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书院学术

Topics in causal inference

Recent advances in causal inference

Matched Sampling for Causal Inference

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