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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..

https://www.bradyneal.com/causal-inference-course#course-textbook2.1. Potential Outcomes and Individual Treatment EffectsThe potential outcome Y(t) denotes what your outcome would be, if you were to take treatment t. A potential outcome Y(t) is distinct from the observed outcome Y in that not all potential outcomes are observed. Rather all potential outcomes can potentially be observed. The one ..

https://www.bradyneal.com/causal-inference-course#course-textbook1.1. Simpson's ParadoxA key ingredient necessary to find Simpson's paradox is the non-uniformity of allocation of people to the groups. Scenario 1If the condition C is a cause of the treatment T, treatment B is more effective at reducing mortality Y. Because having severe condition causes one to be more likely to die (C → Y) and ca..