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Table of contents¶
Selection bias is caused by extra association caused by only part of the population is selected for analysis. Selection bias is caused by conditioning on a common effect of treatment and outcome, even if the treatment actually has no individual causal effect on the outcome.
Confounding is a situation in which the effect of an exposure on an outcome is distorted by the presence of another variable that is associated with both the exposure and the outcome. The confounding effect is a non-causal association that leads to biased estimation of causal effect.
A causal graph consists the following elements:
This chapter considers a system that consists multiple treatment variables.
A null average causal effect does not imply a null average causal effect for a subset of the population. A variable \(V\) is an effect modifier if the average causal effect of \(A\) on \(Y\) differs across levels of \(V\).
In an observational study, the variables are neither assigned nor controlled by the researcher. The researcher only observes the values of the variables. Compared with randomized experiments, observational studies are more limited to the researchers.
According to Causal Inference: What If.
Key concepts: treatment, outcome, average causal effect, potential outcome, consistency, causation-association difference, identifiability
Key concepts: random experiment, exchangeability, effect modification
- 2023-12-07：因果推断，Selection bias，Measurement bias and "noncausal" diagrams
- 2023-11-30：Graphical representation of causal effects
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commit bb742df3c63585f6ce767fece55f23c9304417dd Merge: 33734995 d07b9d57 Author: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Date: Thu Dec 7 21:59:53 2023 +0800 Merge pull request #285 from HuangFuSL/causal-inference-8-9 Update: causal inference chapter 8 & 9