Results 21 to 30 of about 133,258 (310)
An Introduction to Causal Inference [PDF]
The International Journal of Biostatistics, 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. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the ...
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The Future of Causal Inference
American Journal of Epidemiology, 2022 Abstract The past several decades have seen exponential growth in causal inference approaches and their applications. In this commentary, we provide our top-10 list of emerging and exciting areas of research in causal inference. These include methods for high-dimensional data and precision medicine, causal machine learning, causal ...
Nandita, Mitra, Jason, Roy, Dylan, Small
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Implementing Causal Inference in Ecology Through the Structural Causal Model (SCM) Framework [PDF]
, 2022 Ecologists are often interested in understanding causal relationships from ecological data. However, developed methods for causal inference, particularly for observational-based studies are often not taught or applied in ecology.
Arif, Suchinta
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ACM Transactions on Knowledge Discovery from Data, 2021 Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with ...
Liuyi Yao +5 more
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Causal Inference in DotA 2 when estimated through randomized data [PDF]
, 2022 Strategy games could be considered as an amazing playground for using Causal inference methods. The complex nature of the data and the built-in randomization help with testing causal inference in a scenario where in reality it would be hard and expensive.
Avgousti, Stelios (author)
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The mathematics of causal inference [PDF]
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011 I will review concepts, principles, and mathematical tools that were found useful in applications involving causal and counterfactual relationships. This semantical framework, enriched with a few ideas from logic and graph theory, gives rise to a complete, coherent, and friendly calculus of causation that unifies the graphical and counterfactual ...
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CoRR, 2021 We investigate causal inference in the asymptotic regime as the number of variables approaches infinity using an information-theoretic framework. We define structural entropy of a causal model in terms of its description complexity measured by the logarithmic growth rate, measured in bits, of all directed acyclic graphs (DAGs), parameterized by the ...
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Models, identifiability, and estimability in causal inference [PDF]
, 2023 Here we discuss two common but, in our view, misguided assumptions in causal inference. The first assumption is that one requires potential outcomes, directed acyclic graphs (DAGs), or structural causal models (SCMs) for thinking about causal ...
Maclaren, Oliver John, Nicholson, Ruanui
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Proceedings of the AAAI Conference on Artificial Intelligence, 2017 We consider the problem of identifying the causal direction between two discrete random variables using observational data. Unlike previous work, we keep the most general functional model but make an assumption on the unobserved exogenous variable: Inspired by Occam's razor, we assume that the exogenous variable is simple in the true ...
Murat Kocaoglu +3 more
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Revista de la Facultad de Ciencias de la Salud, 2014 Establishing causality has been a problem throughout history of philosophy of science. This paper discusses the philosophy of causal inference along the different school of thoughts and methods: Rationalism, Empiricism, Inductive method, Hypothetical ...
Richard Shoemaker
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