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https://www.bradyneal.com/causal-inference-course#course-textbookHow can we identify causal effects when we are in the presence of unobserved confounding? One popular way is to find and use instrumental variables. An instrument (instrumental variable) Z has three key qualities.  It affects on treatment T, it affects Y only through T, and the effect of Z on Y is unconfounded. We depict these qual..

10hrs with causal inference.. 이제 자야겠다. 힘드러.. 마지막 홍삼!6개월 정도 먹은 것 같다.딱 하나 남으니까 뭔가 아쉽네.그러고 보니.. 요새는 코피가 안났다!?효과가 있는 걸까요..? 더 먹을까요 말까요..? 더 먹어라 말아라 해주세요..

https://www.bradyneal.com/causal-inference-course#course-textbookAll of the methods in Chapter 7 assume that we don't have any unobserved confounding. However, unconfoundedness is an untestable assumption. In observational studies, there could also be some unobserved confounder(s). Therefore, we'd like to know how robust our estimates are to unobserved confounding. The first way we can do is by ..

https://www.bradyneal.com/causal-inference-course#course-textbookOnce we identify some causal estimand by reducing it to a statistical estimand, we still have more work to do. We need to get a corresponding estimate. In this chapter, we'll cover a variety of estimators that we can use to do this. 7.1. PreliminariesWe denote the individual treatment effect (ITE) with τi and average treatment effe..

https://www.bradyneal.com/causal-inference-course#course-textbookIn Section 4.4, we saw that satisfying the backdoor criterion is sufficient to give us identifiability, but is the backdoor criterion also necessary? In other words, is it possible to get identifiability without being able to block all backdoor paths? As an example, consider that we have data generated according to the graph in Fig..

https://www.bradyneal.com/causal-inference-course#course-textbookRandomized experiments are noticeably different from observational studies. In randomized experiments, the experimenter has complete control over the treatment assignment mechanism (how treatment is assigned). For example, in the most simple kind of randomized experiment, the experimenter randomly assigns (e.g. via coin toss) each ..

https://www.bradyneal.com/causal-inference-course#course-textbookCausal models are essential for identification of causal quantities. We described identification as the process of moving from a causal estimand to a statistical estimand. However, to do that, we must have a causal model. 4.1. The do-operator and Interventional DistributionsThe first thing that we will introduce is a mathematical o..

https://www.bradyneal.com/causal-inference-course#course-textbook3.1. Graph TerminologyA graph is a collection of nodes ("vertices") and edges that connect the nodes. Undirected graph: the edges do not have any direction. A directed graph's edges go out of a parent node and into a child node, with the arrows signifying which direction the edges are going. Two nodes are said to be adjacent if the..