Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Abstract
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the special case T={1,2}, A={2,∞}, with Y_i1(a)=0 for all i and a, under Assumption 1, the exact variance of the DID estimator is given by the standard Neyman variance for a randomized experiment.
“Under Assumption 1, the standard results for the variance of the difference in means for a randomized experiments apply (e.g., Neyman [1923/1990], Imbens and Rubin [2015]), and the exact variance of ˆ τ did is,”
From Design-based analysis in Difference-In-Differences settings with staggered adoption (2022) · cites this paper as Imbens and Rubin [2015]
Cites
The publisher hasn’t deposited this paper’s reference list.
Cited by
Sorted by citations
Sensitivity analysis in observational research: Introducing the E-value
VanderWeele, T. J., & Ding, P. (2017) · Annals of Internal Medicine · doi:10.7326/m16-2607 · Cited by 6,350 · see citing works
Double/debiased machine learning for treatment and structural parameters
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. K., & Robins, J. M. (2017) · Econometrics Journal · doi:10.1111/ectj.12097 · Cited by 3,196 · see citing works
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Wager, S., & Athey, S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1319839 · Cited by 2,917 · see citing works
Artificial intelligence and statistics
Yu, B., & Kumbier, K. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700813 · Cited by 2,329 · see citing works
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works
Revisiting Event-Study Designs: Robust and Efficient Estimation
Borusyak, K., Jaravel, X., & Spiess, J. (2024) · Review of Economic Studies · doi:10.1093/restud/rdae007 · Cited by 2,280 · see citing works
Why Propensity Scores Should Not Be Used for Matching
King, G., & Nielsen, R. (2019) · Political Analysis · doi:10.1017/pan.2019.11 · Cited by 1,706 · see citing works
Recursive partitioning for heterogeneous causal effects
Athey, S., & Imbens, G. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510489113 · Cited by 1,586 · see citing works
Self-citationDesigning Difference in Difference Studies: Best Practices for Public Health Policy Research
Wing, C., Simon, K., & Bello-Gomez, R. A. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-013507 · Cited by 1,470 · see citing works
Synthetic Difference-in-Differences
Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021) · American Economic Review · doi:10.1257/aer.20190159 · Cited by 1,518 · see citing works
Self-citationWhen Should You Adjust Standard Errors for Clustering?
Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. M. (2022) · The Quarterly Journal of Economics · doi:10.1093/qje/qjac038 · Cited by 1,460 · see citing works
Self-citationWhy High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs
Gelman, A., & Imbens, G. W. (2017) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2017.1366909 · Cited by 1,383 · see citing works
Self-citationWhen Should You Adjust Standard Errors for Clustering?
Abadie, A., Athey, S. C., Imbens, G. W., & Wooldridge, J. M. (2017) · National Bureau of Economic Research · doi:10.3386/w24003 · Cited by 1,347 · see citing works
Self-citationToward Causal Representation Learning
Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3058954 · Cited by 1,170 · see citing works
Machine Learning Methods That Economists Should Know About
Athey, S., & Imbens, G. W. (2019) · Annual Review of Economics · doi:10.1146/annurev-economics-080217-053433 · Cited by 1,163 · see citing works
Self-citationShifting attention to accuracy can reduce misinformation online
Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021) · Nature · doi:10.1038/s41586-021-03344-2 · Cited by 1,155 · see citing works
Making sense of sensitivity: extending omitted variable bias
Cinelli, C., & Hazlett, C. (2020) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12348 · Cited by 1,088 · see citing works
Inferring causation from time series in Earth system sciences
Runge, J., Bathiany, S., Bollt, E. M., Camps‐Valls, G., Coumou, D., Deyle, E. R., Glymour, C., Kretschmer, M., Mahecha, M. D., Muñoz-Marı́, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10105-3 · Cited by 996 · see citing works
Balancing Covariates via Propensity Score Weighting
Li, F., Morgan, K. L., & Zaslavsky, A. M. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1260466 · Cited by 1,033 · see citing works
Detecting and quantifying causal associations in large nonlinear time series datasets
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., & Sejdinovic, D. (2019) · Science Advances · doi:10.1126/sciadv.aau4996 · Cited by 979 · see citing works
Design-based analysis in Difference-In-Differences settings with staggered adoption
Athey, S., & Imbens, G. W. (2022) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.10.012 · Cited by 917 · see citing works
Self-citationFrom Predictive to Prescriptive Analytics
Bertsimas, D., & Kallus, N. (2020) · Management Science · doi:10.1287/mnsc.2018.3253 · Cited by 784 · see citing works
McMullin, J. L., & Schonberger, B. (2020) · Review of Accounting Studies · doi:10.1007/s11142-019-09525-9 · Cited by 761 · see citing works
A Crash Course in Good and Bad Controls
Cinelli, C., Forney, A., & Pearl, J. (2020) · Sociological Methods & Research · doi:10.1177/00491241221099552 · Cited by 728 · see citing works
Simple Local Polynomial Density Estimators
Cattaneo, M. D., Michael Jansson, C., & Ma, X. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1635480 · Cited by 720 · see citing works
Revisiting event study designs: robust and efficient estimation
(2022) · arXiv (preprint) · doi:10.47004/wp.cem.2022.1122 · Cited by 607 · see citing works
Detecting and quantifying causal associations in large nonlinear time series datasets
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., & Sejdinović, D. (2019) · Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) · Cited by 713 · see citing works
If, Why, and When Subjective Well‐Being Influences Health, and Future Needed Research
Diener, E., Pressman, S. D., Hunter, J. F., & Delgadillo‐Chase, D. (2017) · Applied Psychology Health and Well-Being · doi:10.1111/aphw.12090 · Cited by 721 · see citing works
Algorithmic Fairness: Choices, Assumptions, and Definitions
Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042720-125902 · Cited by 687 · see citing works
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
Choudhury, M. D., Kıcıman, E., Dredze, M. H., Coppersmith, G., & Kumar, M. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858207 · Cited by 682 · see citing works
Regression Discontinuity Designs Using Covariates
Calónico, S., Cattaneo, M. D., Farrell, M. H., & Titiunik, R. (2018) · The Review of Economics and Statistics · doi:10.1162/rest_a_00760 · Cited by 679 · see citing works
Shift-Share Designs: Theory and Inference*
Adão, R., Kolesár, M., & Morales, E. (2019) · The Quarterly Journal of Economics · doi:10.1093/qje/qjz025 · Cited by 667 · see citing works
Causal Inference by using Invariant Prediction: Identification and Confidence Intervals
Peters, J., Bühlmann, P., & Meinshausen, N. (2016) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12167 · Cited by 88 · see citing works
Young, A. (2018) · The Quarterly Journal of Economics · doi:10.1093/qje/qjy029 · Cited by 637 · see citing works
A Review of Domain Adaptation without Target Labels
Kouw, W. M., & Loog, M. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2945942 · Cited by 613 · see citing works
Beyond prediction: using big data for policy problems
Athey, S. (2017) · Science · doi:10.1126/science.aal4321 · Cited by 575 · see citing works
Estimating Treatment Effects with Causal Forests: An Application
Athey, S. C., & Wager, S. (2019) · Observational Studies · doi:10.1353/obs.2019.0001 · Cited by 576 · see citing works
A Practical Introduction to Regression Discontinuity Designs
Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108684606 · Cited by 563 · see citing works
Selective inference for effect modification via the lasso
Nie, X., & Wager, S. (2021) · RePEc: Research Papers in Economics · doi:10.1093/biomet/asaa076 · Cited by 553 · see citing works
Using and Interpreting Fixed Effects Models
Breuer, M., & deHaan, E. (2023) · Journal of Accounting Research · doi:10.1111/1475-679x.12559 · Cited by 548 · see citing works
Unbiased Learning-to-Rank with Biased Feedback
Joachims, T., Swaminathan, A., & Schnabel, T. (2017) · Proceedings of the Tenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3018661.3018699 · Cited by 531 · see citing works
Matrix Completion Methods for Causal Panel Data Models
Athey, S., Bayati, M., Doudchenko, N., Imbens, G., & Khosravi, K. (2018) · doi:10.3386/w25132 · Cited by 497 · see citing works
Self-citationOptimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs
Calónico, S., Cattaneo, M. D., & Farrell, M. H. (2019) · Econometrics Journal · doi:10.1093/ectj/utz022 · Cited by 473 · see citing works
Causal Interpretations of Black-Box Models
Zhao, Q., & Hastie, T. J. (2019) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2019.1624293 · Cited by 465 · see citing works
Implicit Stereotypes: Evidence from Teachers’ Gender Bias*
Carlana, M. (2019) · The Quarterly Journal of Economics · doi:10.1093/qje/qjz008 · Cited by 467 · see citing works
Econometric Methods for Program Evaluation
Abadie, A., & Cattaneo, M. D. (2018) · Annual Review of Economics · doi:10.1146/annurev-economics-080217-053402 · Cited by 434 · see citing works
Using genetic data to strengthen causal inference in observational research
Pingault, J.-B., O’Reilly, P. F., Schoeler, T., Ploubidis, G. B., Rijsdijk, F., & Dudbridge, F. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0020-3 · Cited by 444 · see citing works
Leszczensky, L., & Wolbring, T. (2019) · Sociological Methods & Research · doi:10.1177/0049124119882473 · Cited by 433 · see citing works
Feng, J., Phillips, R. V., Malenica, I., Bishara, A., Hubbard, A. E., Celi, L. A., & Pirracchio, R. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00611-y · Cited by 423 · see citing works
Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis
Doudchenko, N., & Imbens, G. (2016) · doi:10.3386/w22791 · Cited by 410 · see citing works
Self-citationCluster-robust inference: A guide to empirical practice
MacKinnon, J. G., Nielsen, M. Ø., & Webb, M. D. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2022.04.001 · Cited by 407 · see citing works
Policy Learning With Observational Data
Athey, S. C., & Wager, S. (2021) · Econometrica · doi:10.3982/ecta15732 · Cited by 382 · see citing works
When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?
Imai, K., & Kim, I. (2019) · American Journal of Political Science · doi:10.1111/ajps.12417 · Cited by 371 · see citing works
Modeling User Exposure in Recommendation
Liang, D., Charlin, L., McInerney, J., & Blei, D. M. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883090 · Cited by 368 · see citing works
What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory
Lundberg, I., Johnson, R., & Stewart, B. (2021) · American Sociological Review · doi:10.1177/00031224211004187 · Cited by 378 · see citing works
Matching Methods for Causal Inference with Time‐Series Cross‐Sectional Data
Imai, K., Kim, I. S., & Wang, E. H. (2023) · American Journal of Political Science · doi:10.1111/ajps.12685 · Cited by 349 · see citing works
Causal impact of masks, policies, behavior on early COVID-19 pandemic in the U.S
Chernozhukov, V., Kasahara, H., & Schrimpf, P. (2021) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.09.003 · Cited by 403 · see citing works
The Effects of the 1930s HOLC “Redlining” Maps
Aaronson, D., Hartley, D., & Mazumder, B. (2021) · American Economic Journal: Economic Policy · doi:10.1257/pol.20190414 · Cited by 349 · see citing works
Causal machine learning for predicting treatment outcomes
Feuerriegel, S., Frauen, D., Melnychuk, V., Schweisthal, J., Heß, K., Curth, A., Bauer, S., Kilbertus, N., Kohane, I. S., & van der Schaar, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-02902-1 · Cited by 353 · see citing works
The Crowdless Future? Generative AI and Creative Problem-Solving
Boussioux, L., Lane, J. N., Zhang, M., Jaćimović, V., & Lakhani, K. R. (2024) · Organization Science · doi:10.1287/orsc.2023.18430 · Cited by 369 · see citing works
Causal inference and longitudinal data: a case study of religion and mental health
VanderWeele, T. J., Jackson, J. W., & Li, S. (2016) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-016-1281-9 · Cited by 322 · see citing works
Randomization Inference with Stata: A Guide and Software
Heß, S. (2017) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x1701700306 · Cited by 320 · see citing works
Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
Athey, S. C., Imbens, G. W., & Wager, S. (2018) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12268 · Cited by 317 · see citing works
Self-citationOutcome-Wide Longitudinal Designs for Causal Inference: A New Template for Empirical Studies
VanderWeele, T. J., Mathur, M. B., & Chen, Y. (2020) · Statistical Science · doi:10.1214/19-sts728 · Cited by 311 · see citing works
Causal Inference in Accounting Research
Gow, I. D., Larcker, D. F., & REISS, P. C. (2016) · Journal of Accounting Research · doi:10.1111/1475-679x.12116 · Cited by 312 · see citing works
Blankespoor, E., deHaan, E., WERTZ, J., & Zhu, C. (2017) · Journal of Accounting Research · doi:10.1111/1475-679x.12248 · Cited by 308 · see citing works
Balancing Covariates via Propensity Score WeightingPreprint
Li, F., Lock, K., Alan, M., & Zaslavsky, M. (2014) · Cited by 313 · see citing works
Hahn, P. R., Murray, J. S., & Carvalho, C. M. (2017) · Bayesian Analysis · doi:10.1214/19-ba1195 · Cited by 427 · see citing works
Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
Saito, Y., Yaginuma, S., Nishino, Y., Sakata, H., & Nakata, K. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371783 · Cited by 301 · see citing works
Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing
Kohavi, R., Tang, D., & Xu, Y. (2020) · doi:10.1017/9781108653985 · Cited by 297 · see citing works
Intelligent Computing: The Latest Advances, Challenges, and Future
Zhu, S., Yu, T., Xu, T., Chen, H., Dustdar, S., Gigan, S., Gündüz, D., Hossain, E., Jin, Y., Lin, F., et al. (2023) · Intelligent Computing · doi:10.34133/icomputing.0006 · Cited by 319 · see citing works
Causal inference for time series
Runge, J., Gerhardus, A., Varando, G., Eyring, V., & Camps‐Valls, G. (2023) · Nature Reviews Earth & Environment · doi:10.1038/s43017-023-00431-y · Cited by 296 · see citing works
Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019) · Marketing Science · doi:10.1287/mksc.2018.1135 · Cited by 287 · see citing works
Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis
Abadie, A., Athey, S. C., Imbens, G. W., & Wooldridge, J. M. (2020) · Econometrica · doi:10.3982/ecta12675 · Cited by 304 · see citing works
Self-citationRecommendations as Treatments: Debiasing Learning and EvaluationPreprint
Schnabel, T., Swaminathan, A., Singh, A., Chandak, N., & Joachims, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.05352 · Cited by 296 · see citing works
Shareholder Litigation and Corporate Disclosure: Evidence from Derivative Lawsuits
Bourveau, T., Lou, Y., & Wang, R. (2017) · Journal of Accounting Research · doi:10.1111/1475-679x.12191 · Cited by 264 · see citing works
Causal inference in coupled human and natural systems
Ferraro, P. J., Sanchirico, J. N., & Smith, M. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1805563115 · Cited by 262 · see citing works
Kim, S. J., Wang, R. J.-H., & Malthouse, E. C. (2015) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2015.05.004 · Cited by 260 · see citing works
Aronow, P. M., & Samii, C. (2017) · The Annals of Applied Statistics · doi:10.1214/16-aoas1005 · Cited by 347 · see citing works
Causal inference in economics and marketing
Varian, H. R. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510479113 · Cited by 256 · see citing works
Leyrat, C., Seaman, S. R., White, I. R., Douglas, I., Smeeth, L. L., Kim, J., Resche‐Rigon, M., Carpenter, J. R., & Williamson, E. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217713032 · Cited by 254 · see citing works
Regression Discontinuity Designs
Cattaneo, M. D., & Titiunik, R. (2022) · Annual Review of Economics · doi:10.1146/annurev-economics-051520-021409 · Cited by 252 · see citing works
Krieger, N., & Smith, G. D. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw114 · Cited by 255 · see citing works
Cherry Picking with Synthetic Controls
Ferman, B., Pinto, C. C. D. X., & Possebom, V. (2020) · Journal of Policy Analysis and Management · doi:10.1002/pam.22206 · Cited by 244 · see citing works
When Credit Dries Up: Job Losses in the Great Recession
Bentolila, S., Jansen, M., & Jiménez, G. (2017) · Journal of the European Economic Association · doi:10.1093/jeea/jvx021 · Cited by 245 · see citing works
Stable learning establishes some common ground between causal inference and machine learning
Cui, P., & Athey, S. C. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00445-z · Cited by 246 · see citing works
A Penalized Synthetic Control Estimator for Disaggregated Data
Abadie, A., & L’Hour, J. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1971535 · Cited by 240 · see citing works
Causality Inspired Representation Learning for Domain Generalization
Lv, F., Liang, J., Li, S., Zang, B., Liu, C. H., Wang, Z., & Liu, D. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00788 · Cited by 238 · see citing works
Placebo Tests for Causal Inference
Eggers, A. C., Tuñón, G., & Dafoe, A. (2023) · American Journal of Political Science · doi:10.1111/ajps.12818 · Cited by 213 · see citing works
When physics meets machine learning: a survey of physics-informed machine learning
Meng, C., Griesemer, S., Cao, D., Seo, S., & Liu, Y. (2025) · Machine learning for computational science and engineering · doi:10.1007/s44379-025-00016-0 · Cited by 329 · see citing works
Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals
Dahabreh, I. J., & Bibbins-Domingo, K. (2024) · JAMA · doi:10.1001/jama.2024.7741 · Cited by 242 · see citing works
Wu, X., Braun, D., Schwartz, J. D., Kioumourtzoglou, M., & Dominici, F. (2020) · Science Advances · doi:10.1126/sciadv.aba5692 · Cited by 232 · see citing works
Machine Learning for Sociology
Molina, M., & Garip, F. (2019) · Annual Review of Sociology · doi:10.1146/annurev-soc-073117-041106 · Cited by 295 · see citing works
Findley, M. G., Kikuta, K., & Denly, M. (2021) · Annual Review of Political Science · doi:10.1146/annurev-polisci-041719-102556 · Cited by 230 · see citing works
Matching Methods in Practice: Three Examples
Imbens, G. W. (2014) · National Bureau of Economic Research · doi:10.3386/w19959 · Cited by 229 · see citing works
Self-citationTargeting and Privacy in Mobile Advertising
Rafieian, O., & Yoganarasimhan, H. (2018) · Marketing Science · doi:10.1287/mksc.2020.1235 · Cited by 222 · see citing works
Instrumental Variables: An Econometrician's Perspective
Imbens, G. W. (2014) · Statistical Science · doi:10.1214/14-sts480 · Cited by 212 · see citing works
Self-citationThe Language of Social Support in Social Media and Its Effect on Suicidal Ideation Risk
De Choudhury, M., & Kiciman, E. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14891 · Cited by 202 · see citing works
A causal test of the strength of weak ties
Rajkumar, K., Saint-Jacques, G., Bojinov, I., Brynjolfsson, E., & Aral, S. (2022) · Science · doi:10.1126/science.abl4476 · Cited by 216 · see citing works
Causal machine learning for healthcare and precision medicine
Sanchez, P., Voisey, J. P., Xia, T., Watson, H. I., O’Neil, A. Q., & Tsaftaris, S. A. (2022) · Royal Society Open Science · doi:10.1098/rsos.220638 · Cited by 224 · see citing works
- Authors
- Imbens, G. W., & Rubin, D. B.
- Affiliations
- Stanford University (US)
- Harvard University (US)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2015-04-06
- OpenAlex
- W2263423262
- Cited by
- 4,210
- References
- 0
- Citations by year
- 2026: 415 · 2025: 533 · 2024: 521 · 2023: 470 · 2022: 466 · 2021: 476 · 2020: 395 · 2019: 346 · 2018: 238 · 2017: 184 · 2016: 119 · 2015: 30 · 2014: 9 · 2013: 3 · 2012: 1
- FWCI
- 47.73
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Keywords
- causal inference, potential outcomes, causal effects, randomized experiments, observational studies, matching, propensity score methods, instrumental variables, causal assumptions, social sciences, biomedical sciences
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
