We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment). At the end of the course, learners should be able to: 1. Define causal effects using potential outcomes 2. Describe the difference between association and causation 3. Express assumptions with causal graphs 4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting) 5. Identify which causal assumptions are necessary for each type of statistical method So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!
A Crash Course in Causality: Inferring Causal Effects from Observational Data
提供：
A Crash Course in Causality: Inferring Causal Effects from Observational Data
このコースについて
Learner Career Outcomes
50%
23%
習得するスキル
Learner Career Outcomes
50%
23%
100%オンライン
柔軟性のある期限
中級レベル
約26時間で修了
英語
シラバス - 本コースの学習内容
Welcome and Introduction to Causal Effects
This module focuses on defining causal effects using potential outcomes. A key distinction is made between setting/manipulating values and conditioning on variables. Key causal identifying assumptions are also introduced.
Confounding and Directed Acyclic Graphs (DAGs)
This module introduces directed acyclic graphs. By understanding various rules about these graphs, learners can identify whether a set of variables is sufficient to control for confounding.
Matching and Propensity Scores
An overview of matching methods for estimating causal effects is presented, including matching directly on confounders and matching on the propensity score. The ideas are illustrated with data analysis examples in R.
Inverse Probability of Treatment Weighting (IPTW)
Inverse probability of treatment weighting, as a method to estimate causal effects, is introduced. The ideas are illustrated with an IPTW data analysis in R.
65件のレビュー
A Crash Course in Causality: Inferring Causal Effects from Observational Data からの人気レビュー
I really enjoyed this course, the pace could be more even in parts. Sometimes the pace could be more even and some more books/reference material for further study would be nice.
The material is great. Just wished the professor was more active in the discussion forum. Have not showed up in the forum for weeks. At least there should be a TA or something.
講師
Jason A. Roy, Ph.D.
Professor of Biostatisticsペンシルベニア大学（University of Pennsylvania）について
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