arXiv 11 Jul 2024 · Econometrics · publishedZeitschrift für schweizerische Statistik und Volkswirtschaft/Schweizerische Zeitschrift für Volkswirtschaft und Statistik/Swiss journal of economics and statistics (2024) · 9 citations (OpenAlex)
arXiv:2407.08602 · PDF · DOI · OpenAlex · Extracted main text
In 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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pearl, J (2000) Causality: Models, Reasoning, and Inference | 1.000 | 5 | 3 | 100% |
| 2 | Glymour, C., K. Zhang, and P. Spirtes (2019) Review of causal discovery methods based on graphical models | 0.737 | 3 | 2 | 100% |
| 3 | Abadie, A. and M. D. Cattaneo (2018) Econometric methods for program evaluation | 0.644 | 2 | 2 | 100% |
| 4 | Imbens, G. W. (2004, Feb.) (2004) Nonparametric estimation of average treatment effects under exogeneity: a review | 0.644 | 2 | 2 | 100% |
| 5 | Imbens, G. W. and J. M. Wooldridge (2009) Recent developments in the econometrics of program evaluation | 0.644 | 2 | 2 | 100% |
| 6 | Frölich, M. and S. Sperlich (2019) Impact Evaluation: Treatment Effects and Causal Analysis | 0.644 | 2 | 2 | 100% |
| 7 | Peters, J., D. Janzing, and B. Schölkopf (2017) Elements of causal inference: foundations and learning algorithms | 0.644 | 2 | 2 | 100% |
| 8 | Spirtes, P., C. N. Glymour, and R. Scheines (2000) Causation, prediction, and search | 0.644 | 2 | 2 | 100% |
| 9 | Peters, J., P. Bühlmann, and N. Meinshausen (2015) Causal inference using invariant prediction: identification and confidence intervals | 0.585 | 3 | 1 | 100% |
| 10 | Huber, M (2023) Causal analysis: Impact evaluation and Causal Machine Learning with applications in R self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 51 scored citations.