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An introduction to causal discovery
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AbstractIn social sciences and economics, causal inference traditionally focuses on assessing the impact of predefined treatments (or interventions) on predefined outcomes, such as the effect of education programs on earnings. Causal discovery, in contrast, aims to uncover causal relationships among multiple variables in a data-driven manner, by investigating statistical associations rather than relying on predefined causal structures. This approach, more common in computer science, seeks to understand causality in an entire system of variables, which can be visualized by causal graphs. This survey provides an introduction to key concepts, algorithms, and applications of causal discovery from the perspectives of economics and social sciences. It covers fundamental concepts like d-separation, causal faithfulness, and Markov equivalence, sketches various algorithms for causal discovery and discusses the back-door and front-door criteria for identifying causal effects. The survey concludes with more specific examples of causal discovery, e.g., for learning all variables that directly affect an outcome of interest and/or testing identification of causal effects in observational data.
Title: An introduction to causal discovery
Description:
AbstractIn social sciences and economics, causal inference traditionally focuses on assessing the impact of predefined treatments (or interventions) on predefined outcomes, such as the effect of education programs on earnings.
Causal discovery, in contrast, aims to uncover causal relationships among multiple variables in a data-driven manner, by investigating statistical associations rather than relying on predefined causal structures.
This approach, more common in computer science, seeks to understand causality in an entire system of variables, which can be visualized by causal graphs.
This survey provides an introduction to key concepts, algorithms, and applications of causal discovery from the perspectives of economics and social sciences.
It covers fundamental concepts like d-separation, causal faithfulness, and Markov equivalence, sketches various algorithms for causal discovery and discusses the back-door and front-door criteria for identifying causal effects.
The survey concludes with more specific examples of causal discovery, e.
g.
, for learning all variables that directly affect an outcome of interest and/or testing identification of causal effects in observational data.
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