The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Causality: models, reasoning, and inference
Abstract
This book seeks to integrate research on cause and effect inference from cognitive science, econometrics, epidemiology, philosophy, and statistics. It puts forward the work of its author, his collaborators, and others over the past two decades as a new account of cause and effect inference that can aid practical researchers in many fields, including econometrics. Pearl adheres to several propositions on cause and effect inference. Though cause and effect relations are fundamentally deterministic (he explicitly excludes quantum mechanical phenomena from his concept of cause and effect), cause and effect analysis involves probability language. Probability language helps to convey uncertainty about cause and effect relations but is insufficient to fully express those relations. In addition to conditional probabilities of events, cause and effect analysis requires graphs or diagrams and a language that distinguishes intervention or manipulation from observation. Cause and effect analysis also requires counterfactual reasoning and causal assumptions in addition to observations and statistical assumptions. Chapter 1 sketches some of the ingredients of the new approach to cause and effect inference: probability theory, graphs, Bayesian causal networks, causal models, and causal and statistical terminology. Chapter 2 builds the elements of Chapter 1 into a theory of inferred causation. Chapter 3 focuses on causal diagrams and identifying causal effects. Chapter 4 studies intervention or manipulation and direct causal effects. Chapter 5 considers causality and structural equation models. Chapter 6 examines Simpson's paradox and confounding. Chapter 7 blends structural modeling with counterfactual reasoning. Chapter 8 is an approach to imperfect random assignment experiments through bounding effects and counterfactuals. Chapter 9 analyzes notions of necessary cause and sufficient cause. Chapter 10 explicates a concept of single event causality. The epilogue is a public lecture that Pearl gave at UCLA that, in mostly not too technical language, places the new approach to causality within the long history of thought on the subject. The interdisciplinary nature of the book, a great strength, at times makes it difficult to read because its theory of inferred causation blends the languages
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment
Rubin, D. B. (1980) · Journal of the American Statistical Association · doi:10.2307/2287653 · Cited by 1,368
Cited by
Un sistema de clasificación de los diseños de investigación en psicología
Ato, M., López-García, J. J., & Benavente, A. (2013) · Anales de Psicología · doi:10.6018/analesps.29.3.178511 · Cited by 2,254
Explainable machine learning for scientific insights and discoveries
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040
Homophily and contagion are generically confounded in observational social network studies
Shalizi, C. R., & Thomas, A. C. (2011) · Sociological Methods & Research · doi:10.1177/0049124111404820 · Cited by 994
“The “easy” solution is to use expert knowledge of the system to identify all causally relevant variables, measure a sufficient set of them, and adjust for them appropriately (Morgan and Winship, 2007; Pearl, 2000; Spirtes et al., 2001).”
MethodLanger, M., Oster, D., Speith, T., Hermanns, H., Kästner, L., Schmidt, E., Sesing, A., & Baum, K. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103473 · Cited by 652
Causal inference in perception
Shams, L., & Beierholm, U. R. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.07.001 · Cited by 403
Holzinger, A., Malle, B., Saranti, A., & Pfeifer, B. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.01.008 · Cited by 352
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2014) · Oxford Handbooks Online · doi:10.1093/oxfordhb/9780199755615.013.007 · Cited by 305
Friston, K. J., Li, B., Daunizeau, J., & Stephan, K. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.039 · Cited by 283
Chou, Y.-L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.003 · Cited by 302
On the definition of a confounder
VanderWeele, T. J., & Shpitser, I. (2013) · The Annals of Statistics · doi:10.1214/12-aos1058 · Cited by 268
Hesser, H. (2015) · Internet Interventions · doi:10.1016/j.invent.2015.02.003 · Cited by 192
Love, P. E. D., Ahiaga-Dagbui, D. D., & Irani, Z. (2016) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2016.08.007 · Cited by 138
Bayesian networks for fMRI: A primer
Mumford, J. A., & Ramsey, J. D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.020 · Cited by 132
Genetics, lifestyle and longevity: Lessons from centenarians
Govindaraju, D., Atzmon, G., & Barzilai, N. (2015) · Applied & Translational Genomics · doi:10.1016/j.atg.2015.01.001 · Cited by 128
Narratives About the Macroeconomy
Andre, P., Haaland, I., Roth, C., & Wohlfart, J. (2023) · doi:10.2139/ssrn.4506321 · Cited by 100
Hell, F., Palleis, C., Mehrkens, J. H., Koeglsperger, T., & Bötzel, K. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00314 · Cited by 75
Mitigating risk of failure in information technology projects: Causes and mechanisms
Schmidt, J. (2023) · Project Leadership and Society · doi:10.1016/j.plas.2023.100097 · Cited by 56
Uncertainty in Measurement and Total Error
Theodorsson, E. (2017) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2016.09.002 · Cited by 54
Lamboni, M., & Kucherenko, S. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107519 · Cited by 44
Falkenström, F. (2024) · Clinical Psychology Review · doi:10.1016/j.cpr.2024.102435 · Cited by 39
Causal composition: Structural differences among dynamically equivalent systems
Albantakis, L., & Tononi, G. (2019) · Entropy · doi:10.3390/e21100989 · Cited by 30
“However, transfer entropy still relies on observational data, while a causal approach generally requires perturbational data [53] (although observational data is sufficient for causal inference in ER systems since they cycle through all their possible states).”
BackgroundZiegler, C. R., Webb, J. A., Norton, S. B., Pullin, A. S., & Melcher, A. H. (2015) · Ecological Indicators · doi:10.1016/j.ecolind.2015.01.003 · Cited by 18
Controlling Selection Bias in Causal Inference
Bareinboim, E., & Pearl, J. (2011) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v25i1.8056 · Cited by 17
Grounding as a Collaborative Process
Benotti, L., & Blackburn, P. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.41 · Cited by 14
Dräger, J., & Pforr, K. (2022) · Advances in Life Course Research · doi:10.1016/j.alcr.2022.100476 · Cited by 8
Dissociating Artificial Intelligence from Artificial Consciousness
Findlay, G., Marshall, W., Albantakis, L., David, I., Mayner, W. G., Koch, C., & Tononi, G. (2024) · arXiv · doi:10.48550/arxiv.2412.04571 · Cited by 41
“This is somewhat akin to “screening-off” in other causal inference frameworks [112–115].”
Robust agents learn causal world models
Richens, J., & Everitt, T. (2024) · arXiv · doi:10.48550/arxiv.2402.10877 · Cited by 5
“There are several alternative models and formalisms that are studied in the literature, including structural equation models (Pearl, 2009) and the Neyman-Rubin causal models (Rubin, 2005), and results can be straightforwardly adapted to these.”
BackgroundInfluential
Metadata
- Venue
- Choice Reviews Online
- Publisher
- American Library Association
- Published
- 2010-03-01
- Volume
- 47
- Issue
- 07
- Pages
- 47-3771-47-3771
- ISSN
- 0009-4978 (ISSN-L)
- 1523-8253
- OpenAlex
- W3145915828
- Cited by
- 1,014
- References
- 1
- Type
- book review
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
