Link: https://en.wikipedia.org/wiki/Causal_inference
Description: WEBCausal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed.
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Link: https://towardsdatascience.com/a-complete-guide-to-causal-inference-8d5aaca68a47
Description: WEBFeb 21, 2022 · What is Causal Inference? Fortunately, people have been working on this fundamental question: What is causation, and how do we know when A causes B? One of the key players in this field, Judea Pearl, often reiterates that understanding causality is key to good decision-making.
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Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2836213/
Description: WEBJan 1, 2010 · This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data.
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Link: https://towardsdatascience.com/causal-inference-what-why-and-how-d7336b0081ec
Description: WEBNov 23, 2020 · A causal relationship is so powerful that it gives enough confidence in making decisions, preventing losses, solving optimal solutions, and so forth. In this article, I will discuss what causality is, why we need to discover causal relationships, and the common techniques to conduct causal inference.
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Link: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
Description: WEBCausal Inference: What If (the book) Jamie Robins and I have written a book that provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in several …
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Link: https://web.stanford.edu/~swager/stats361.pdf
Description: WEBcausal e ect of the treatment on the i-th unit is then1 i= Y i(1) Y i(0): (1.1) The fundamental problem in causal inference is that only one treatment can be assigned to a given individual, and so only one of Y i(0) and Y i(1) can ever be observed. Thus, i can never be observed.
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Link: https://web.cs.ucla.edu/~kaoru/primer-complete-2019.pdf
Description: WEBficial intelligence, causal inference and philosophy of science. He is a Co-Founder and Editor of the Journal of Causal Inference and the author of three landmark books in inference-related areas. His latest book, Causality: Models, Reasoning and Inference (Cambridge, 2000, 2009), hasintroducedmany of themethodsused in moderncausal analysis.
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Link: http://www.statslab.cam.ac.uk/~qz280/talk/ssrmp-2020/slides.pdf
Description: WEBCausal inference is driven by applications and is at the core of statistics (the science of using information discovered from collecting, organising, and studying numbers|Cambridge Dictionary). Many origins of causal inference Biology and genetics; Agriculture; Epidemiology, public health, and medicine;
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Link: https://www.sciencedirect.com/topics/social-sciences/causal-inference
Description: WEBIntroduction: Causal Inference as a Comparison of Potential Outcomes. Causal inference refers to an intellectual discipline that considers the assumptions, study designs, and estimation strategies that allow researchers to draw causal conclusions based on data.
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Link: https://dl.acm.org/doi/10.1145/3444944
Description: WEBMay 10, 2021 · Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks.
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