Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

Reshaping the structure of the World Trade Network: a pivotal role for China?

Hoang, V. P., Piccardi, C., & Tajoli, L. (2023) · Applied Network Science · doi:10.1007/s41109-023-00560-9 · Cited by 21

The Unfulfilled Promise of Longitudinal Designs for Causal Inference

Loh, W. W., & Ren, D. (2023) · Collabra Psychology · doi:10.1525/collabra.89142 · Cited by 23

Deep Causal Reasoning for Recommendations

Zhu, Y., Yi, J., Xie, J., & Chen, Z. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3653985 · Cited by 21

Efficient propensity score regression estimators of multivalued treatment effects for the treated

Lee, Y. (2018) · Journal of Econometrics · doi:10.1016/j.jeconom.2018.02.002 · Cited by 21

Nudging museums attendance: a field experiment with high school teens

Lattarulo, P., Mariani, M., & Razzolini, L. (2016) · Journal of Cultural Economics · doi:10.1007/s10824-016-9285-6 · Cited by 26

Zoning effects on housing change vary with income, based on a four-decade panel model after propensity score matching

Locke, C. M., Butsic, V., & Rissman, A. R. (2017) · Land Use Policy · doi:10.1016/j.landusepol.2017.03.019 · Cited by 21

Examining the effects of residential self-selection on internal and external validity: an interaction moderation analysis using structural equation modeling

Gim, T. T. (2017) · Transportation Letters · doi:10.1080/19427867.2017.1338544 · Cited by 21

A model averaging approach for estimating propensity scores by optimizing balance

Xie, Y., Zhu, Y., Cotton, C. A., & Wu, P. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217715487 · Cited by 24

Maximizing Intervention Effectiveness

Gupta, V., Han, B. R., Kim, S., & Paek, H. (2020) · Management Science · doi:10.1287/mnsc.2019.3537 · Cited by 21

The financing of immigrant-owned firms in Canada

Ostrovsky, Y., Picot, G., & Leung, D. (2018) · Small Business Economics · doi:10.1007/s11187-018-0020-x · Cited by 21

Writing a discussion section: how to integrate substantive and statistical expertise

Höfler, M., Venz, J., Trautmann, S., & Miller, R. E. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0490-1 · Cited by 21

“Stepping up or stepping down?“: The earnings differences associated with Swedish temporary workers’ employment sequences

Reichenberg, O., & Berglund, T. (2019) · Social Science Research · doi:10.1016/j.ssresearch.2019.04.007 · Cited by 21

Does e-cigarette experimentation increase the transition to daily smoking among young ever-smokers in France?

Chyderiotis, S., Benmarhnia, T., Beck, F., Spilka, S., & Legleye, S. (2020) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2020.107853 · Cited by 21

Causal Models for Dynamical Systems

Peters, J., Bauer, S., & Pfister, N. (2022) · ACM eBooks · doi:10.1145/3501714.3501752 · Cited by 23

Minimax designs for causal effects in temporal experiments with treatment habituation

Basse, G., Ding, Y., & Toulis, P. (2022) · Biometrika · doi:10.1093/biomet/asac024 · Cited by 20

Another Look at the Lady Tasting Tea and Differences Between Permutation Tests and Randomisation Tests

Hemerik, J., & Goeman, J. J. (2020) · International Statistical Review · doi:10.1111/insr.12431 · Cited by 21

Higher-order interactions in statistical physics and machine learning: A model-independent solution to the inverse problem at equilibrium

Beentjes, S. V., & Khamseh, A. (2020) · Physical review. E · doi:10.1103/physreve.102.053314 · Cited by 20

The Associations Between Music Training, Musical Working Memory, and Visuospatial Working Memory

Silas, S. J., Müllensiefen, D., Gelding, R. W., Frieler, K., & Harrison, P. M. C. (2022) · Music Perception An Interdisciplinary Journal · doi:10.1525/mp.2022.39.4.401 · Cited by 27

Evaluating a Key Instrumental Variable Assumption Using Randomization Tests

Branson, Z., & Keele, L. J. (2020) · American Journal of Epidemiology · doi:10.1093/aje/kwaa089 · Cited by 19

Compared to What? Variation in the Impacts of Early Childhood Education by Alternative Care-Type SettingsPreprint

Feller, A., Grindal, T., Miratrix, L. W., & Page, L. C. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2534811 · Cited by 21

Causal Modeling with Feedback Fuzzy Cognitive Maps

Osoba, O., & Kosko, B. (2019) · doi:10.1002/9781119485001.ch25 · Cited by 21

The Impact of Subscription Programs on Customer PurchasesPreprint

Iyengar, R., Park, Y.-H., & Yu, Q. D. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3529681 · Cited by 21

Causal Inference with Genetic Data: Past, Present, and FuturePreprint

Pingault, J., Richmond, R. C., & Smith, G. D. (2021) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a041271 · Cited by 25

Gender Preferences in Job Vacancies and Workplace Gender Diversity

Card, D., Colella, F., & Lalive, R. (2021) · National Bureau of Economic Research · doi:10.3386/w29350 · Cited by 21

Causal inference in the absence of positivity: The role of overlap weights

Matsouaka, R., & Zhou, Y. (2024) · Biometrical Journal · doi:10.1002/bimj.202300156 · Cited by 21

Transparency challenges in policy evaluation with causal machine learning: improving usability and accountability

Rehill, P., & Biddle, N. (2024) · Data & Policy · doi:10.1017/dap.2024.35 · Cited by 20

Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network

Bui, N., Yen, J., & Honavar, V. (2016) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2016.2591880 · Cited by 20

Informative Subspace Learning for Counterfactual Inference

Chang, Y., & Dy, J. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10800 · Cited by 22

Identifying and estimating the effects of a mandatory billing demand charge

Öhrlund, I., Schultzberg, M., & Bartusch, C. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.028 · Cited by 23

Practical valid inferences for the two-sample binomial problem

Fay, M. P., & Hunsberger, S. (2021) · Statistics Surveys · doi:10.1214/21-ss131 · Cited by 21

Reducing Hunger with Payments for Environmental Services ( PES ): Experimental Evidence from Burkina Faso

Adjognon, G. S., Soest, D. P. V., & Guthoff, J. (2020) · American Journal of Agricultural Economics · doi:10.1111/ajae.12150 · Cited by 21

Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs

Schochet, P. Z., Pashley, N. E., Miratrix, L. W., & Kautz, T. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1906685 · Cited by 21

Multiply robust matching estimators of average and quantile treatment effects

Yang, S., & Zhang, Y. (2022) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12585 · Cited by 22

Causal Optimal Transport for Treatment Effect Estimation

Li, Q., Wang, Z., Liu, S., Li, G., & Xu, G. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3118542 · Cited by 20

Early Versus Later Initiation of Parenteral Nutrition for Very Preterm Infants: A Propensity Score Matched Observational Study

Uthaya, S. N., Longford, N. T., Battersby, C., Ougham, K., Lanoue, J., & Modi, N. (2021) · Archives of Disease in Childhood Fetal & Neonatal · doi:10.1136/archdischild-2021-322383 · Cited by 20

Improved agricultural input delivery systems for enhancing technology adoption: evidence from a field experiment in Ethiopia

Yitayew, A., Abdulai, A., & Yigezu, Y. A. (2022) · European Review of Agricultural Economics · doi:10.1093/erae/jbac006 · Cited by 22

School leaders’ self-efficacy and job satisfaction over nine annual waves: A substantive-methodological synergy juxtaposing competing models of directional ordering

Marsh, H. W., Lüdtke, O., Pekrun, R., Parker, P. D., Murayama, K., Guo, J., Basarkod, G., Dicke, T., Donald, J. N., & Morin, A. J. S. (2023) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2023.102170 · Cited by 19

TikTok and Civic Activity Among Young Adults

Moffett, K. W., & Rice, L. L. (2023) · Social Science Computer Review · doi:10.1177/08944393231188470 · Cited by 41

A Tutorial on Causal Inference in Longitudinal Data With Time-Varying Confounding Using G-Estimation

Loh, W. W., & Ren, D. (2023) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459231174029 · Cited by 23

GraphCA: Learning from Graph Counterfactual Augmentation for Knowledge Tracing

Wang, X., Zhao, S., Guo, L., Zhu, L., Cui, C., & Xu, L. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123678 · Cited by 21

Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop

Bunyavanich, S., Becker, P. M., Altman, M. C., Lasky-Su, J. A., Ober, C., Zengler, K., Berdyshev, E. V., Bonneau, R., Chatila, T. A., Chatterjee, N., et al. (2024) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2024.01.014 · Cited by 19

Time-Varying Treatments in Observational Studies: Marginal Structural Models of the Effects of Early Grade Retention on Math Achievement

Vandecandelaere, M., Vansteelandt, S., Fraine, B. D., & Damme, J. V. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1155146 · Cited by 26

Assumption Lean Regression

Berk, R. A., Buja, A., Brown, L., George, E. I., Kuchibhotla, A. K., Su, W., & Zhao, L. H. (2019) · The American Statistician · doi:10.1080/00031305.2019.1592781 · Cited by 20

Robust Text Classification under Confounding Shift

Landeiro, V., & Culotta, A. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11248 · Cited by 21

Ensuring Causal, Not Casual, Inference

Musci, R. J., & Stuart, E. A. (2019) · Prevention Science · doi:10.1007/s11121-018-0971-9 · Cited by 20

Does the market model provide a good counterfactual for event studies in finance?

Castro-Iragorri, C. (2019) · Financial markets and portfolio management · doi:10.1007/s11408-019-00325-4 · Cited by 22

A Viable Alternative When Propensity Scores Fail: Evaluation of Inverse Propensity Weighting and Sequential G-Estimation in a Two-Wave Mediation Model

Valente, M. J., MacKinnon, D. P., & Mazza, G. L. (2019) · Multivariate Behavioral Research · doi:10.1080/00273171.2019.1614429 · Cited by 20

Entropy Learning for Dynamic Treatment Regimes

Jiang, B., Song, R., Li, J., & Zeng, D. (2018) · Statistica Sinica · doi:10.5705/ss.202018.0076 · Cited by 20

A Statistical Roadmap for Journey from Real-World Data to Real-World Evidence

Fang, Y., Wang, H., & He, W. (2019) · Therapeutic Innovation & Regulatory Science · doi:10.1007/s43441-019-00008-2 · Cited by 20

CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

Zhang, W., Panum, T. K., Jha, S., Chalasani, P., & Page, D. (2020) · PubMed · doi:10.48550/arxiv.2002.07906 · Cited by 20

A Counterfactual Framework for Seller-Side A/B Testing on Marketplaces

Ha−Thuc, V., Dutta, A., Mao, R., Wood, M., & Liu, Y. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401434 · Cited by 20

Public cooperation and the police: Do calls-for-service increase after homicides?

Brantingham, P. J., & Uchida, C. D. (2021) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2021.101785 · Cited by 23

Randomization Tests to Assess Covariate Balance When Designing and Analyzing Matched Datasets

Branson, Z. (2021) · Observational Studies · doi:10.1353/obs.2021.0031 · Cited by 22

Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning

Greenbury, S. F., Ougham, K., Wu, J., Battersby, C., Gale, C., Modi, N., & Angelini, E. D. (2021) · Scientific Reports · doi:10.1038/s41598-021-85878-z · Cited by 20

A regularity theory of causality for the social sciences

Mahoney, J., & Acosta, L. (2021) · Quality & Quantity · doi:10.1007/s11135-021-01190-y · Cited by 24

Sampling-based vs. Design-based Uncertainty in Regression AnalysisPreprint

Abadie, A., Athey, S. C., Imbens, G. W., & Wooldridge, J. M. (2017) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1706.01778 · Cited by 20

Quasi-Experimental Methods in Environmental Economics: Opportunities and Challenges

Deschênes, O., & Meng, K. C. (2018) · National Bureau of Economic Research · doi:10.3386/w24903 · Cited by 19

Sufficient Representations for Categorical VariablesPreprint

Johannemann, J., Hadad, V., Athey, S. C., & Wager, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.09874 · Cited by 39

Effect of emergency winter homeless shelters on property crime

Faraji, S.-L., Ridgeway, G. K., & Wu, Y. (2018) · Journal of Experimental Criminology · doi:10.1007/s11292-017-9320-4 · Cited by 19

Inferring causal networks of dynamical systems through transient dynamics and perturbation

Stepaniants, G., Brunton, B. W., & Kutz, J. N. (2020) · Physical review. E · doi:10.1103/physreve.102.042309 · Cited by 19

Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes

Suk, Y., Kim, J.-S., & Kang, H. (2020) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998620951983 · Cited by 18

Sufficient dimension reduction for average causal effect estimation

Cheng, D., Li, J., Liu, L., Le, T. D., Liu, J., & Yu, K. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00832-5 · Cited by 22

At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?

de Chaisemartin, C., & Ramírez, J. (2023) · American Economic Journal Applied Economics · doi:10.1257/app.20210252 · Cited by 19

A compositional mediation model for a binary outcome: Application to microbiome studies

Sohn, M. B., Lu, J., & Li, H. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab605 · Cited by 17

Causal inference for multiple treatments using fractional factorial designs

Pashley, N. E., & Bind, M. (2022) · Canadian Journal of Statistics · doi:10.1002/cjs.11734 · Cited by 26

Applied usage and performance of statistical matching in bibliometrics: The comparison of milestone and regular papers with multiple measurements of disruptiveness as an empirical example

Bittmann, F., Tekles, A., & Bornmann, L. (2021) · Quantitative Science Studies · doi:10.1162/qss_a_00158 · Cited by 24

Doubly robust identification for causal panel data models

Arkhangelsky, D., & Imbens, G. W. (2022) · Econometrics Journal · doi:10.1093/ectj/utac019 · Cited by 27

Aggressive policing and undermined legitimacy: assessing the impact of police stops at gunpoint on perceptions of police in São Paulo, Brazil

Oliveira, T. R. (2022) · Journal of Experimental Criminology · doi:10.1007/s11292-022-09527-9 · Cited by 23

Dynamic and cyclic relationships between employees' intrinsic and extrinsic motivation: Evidence from dynamic multilevel modeling analysis

Li, R., Liu, H., Chen, Z., & Wang, Y. (2022) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2022.103813 · Cited by 24

Observational cohort study of use of caffeine in preterm infants and association between early caffeine use and neonatal outcomes

Szatkowski, L. C., Fateh, S., Abramson, J., Kwok, T. C., Sharkey, D., Budge, H., & Ojha, S. (2023) · Archives of Disease in Childhood Fetal & Neonatal · doi:10.1136/archdischild-2022-324919 · Cited by 19

Causal Inference with Conditional Instruments Using Deep Generative Models

Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., & Le, T. D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i6.25869 · Cited by 19

Fixed Effects and the Generalized Mundlak Estimator

Arkhangelsky, D., & Imbens, G. W. (2023) · The Review of Economic Studies · doi:10.1093/restud/rdad089 · Cited by 19

User-generated content affects urban park use: Analysis of direct and moderating effects

Wei, D., Liu, M., Grekousis, G., Wang, Y., & Lü, Y. (2023) · Urban forestry & urban greening · doi:10.1016/j.ufug.2023.128158 · Cited by 19

Bystanders of Online Moderation: Examining the Effects of Witnessing Post-Removal Explanations

Jhaver, S., Rathi, H., & Saha, K. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642204 · Cited by 23

Going Public and the Internal Organization of the Firm

Bias, D., Lochner, B., Obernberger, S., & Sevilir, M. (2022) · The Journal of Finance · doi:10.1111/jofi.70012 · Cited by 19

Recent development of propensity score methods in observational studies: Multi-categorical treatment, causal mediation, and heterogeneity

Hu, A., & Mustillo, S. (2015) · Current Sociology · doi:10.1177/0011392115589599 · Cited by 20

On causal estimation using $U$-statistics

Mao, L. (2017) · Biometrika · doi:10.1093/biomet/asx071 · Cited by 19

Bunching at 3 Percent: The Maastricht Fiscal Criterion and Government Deficits

Caselli, F. G., & Wingender, P. (2018) · IMF Working Paper · doi:10.5089/9781484371732.001 · Cited by 20

ACE: Adaptively Similarity-Preserved Representation Learning for Individual Treatment Effect Estimation

Yao, L., Li, S., Li, Y., Huai, M., Gao, J., & Zhang, A. (2019) · Proceedings · doi:10.1109/icdm.2019.00186 · Cited by 19

An Experimental Study of Structural Diversity in Social Networks

Lasky-Su, J. A., Kamath, K. Y., Sharma, A., Ugander, J. H., & Goel, S. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7332 · Cited by 19

AdverTiming Matters: Examining User Ad Consumption for Effective Ad Allocations on Social Media

Saha, K., Liu, Y., Vincent, N., Chowdhury, F. A., Neves, L., Shah, N., & Bos, M. W. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445394 · Cited by 22

Memory care reduces nursing home admissions among assisted‐living residents with dementia

Cornell, P. Y., Zhang, W., Smith, L., Rahman, M., Grabowski, D. C., Carder, P., & Thomas, K. S. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12513 · Cited by 19

Running Randomized Field Experiments for Energy Efficiency Programs: A Practitioner�s Guide

Gandhi, R., Knittel, C. R., Pedro, P., & Wolfram, C. (2016) · Economics of Energy and Environmental Policy · doi:10.5547/2160-5890.5.2.rgan · Cited by 19

The Effect of Retail Distribution on Sales of Alcoholic Beverages

Friberg, R., & Sanctuary, M. (2017) · Marketing Science · doi:10.1287/mksc.2017.1038 · Cited by 19

Matching algorithms for causal inference with multiple treatments

Scotina, A. D., & Gutman, R. (2019) · Statistics in Medicine · doi:10.1002/sim.8147 · Cited by 20

Does studying abroad enhance employability?

Liwiński, J. (2018) · Economics of Transition and Institutional Change · doi:10.1111/ecot.12203 · Cited by 19

Assessing external validity

Bo, H., & Galiani, S. (2021) · Research in Economics · doi:10.1016/j.rie.2021.06.005 · Cited by 19

Artificial mental phenomena

Liang, L., & Acuna, D. E. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3375623 · Cited by 18

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal InferencePreprint

Wang, T., Morucci, M., Awan, M. U., Liu, Y., Roy, S., Rudin, C. D., & Volfovsky, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.06315 · Cited by 22

Exposure to Opposing Views can Increase Political Polarization: Evidence from a Large-Scale Field Experiment on Social MediaPreprint

Bail, C. A., Argyle, L. P., Brown, T., Bumpus, J. P., Chen, H., Hunzaker, M. B. F., Lee, J., Mann, M., Merhout, F., & Volfovsky, A. (2018) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/4ygux · Cited by 23

Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term OutcomesPreprint

Athey, S. C., Chetty, R., & Imbens, G. W. (2020) · RePEc: Research Papers in Economics · Cited by 21

Why Experimental Balance Is Still a Reason to Randomize

Martinez, M., & Teira, D. (2021) · The British Journal for the Philosophy of Science · doi:10.1086/716096 · Cited by 19

Causal inference in the medical domain: a survey

Wu, X., Peng, S., Li, J., Zhang, J., Sun, Q., Li, W., Qian, Q., Liu, Y., & Guo, Y. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05338-9 · Cited by 21

SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data

Dong, M., Su, D. G., Kluger, H. M., Fan, R., & Kluger, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-58089-7 · Cited by 25

Bayesian multi-view models for member-job matching and personalized skill recommendations

Maurya, A., & Telang, R. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258045 · Cited by 18

A review of single-source unsupervised domain adaptation.

Kouw, W. M., & Loog, M. (2019) · Cited by 18

Randomization Tests for Peer Effects in Group Formation Experiments

Basse, G., Ding, P., Feller, A., & Toulis, P. (2024) · Econometrica · doi:10.3982/ecta20134 · Cited by 30

Experimenting first with e‐cigarettes versus first with cigarettes and transition to daily cigarette use among adolescents: the crucial effect of age at first experiment

Legleye, S., Aubin, H., Falissard, B., Beck, F., & Spilka, S. (2020) · Addiction · doi:10.1111/add.15330 · Cited by 18

Assessing Sensitivity to Unconfoundedness: Estimation and Inference

Masten, M. A., Poirier, A., & Zhang, L. (2023) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2023.2183212 · Cited by 24

Household access to water and education for girls: The case of villages in hilly and mountainous areas of Nepal

Dhital, R. P., Ito, T., Kaneko, S., Komatsu, S., & Yoshida, Y. (2021) · Oxford Development Studies · doi:10.1080/13600818.2021.1965978 · Cited by 18

Causal Inference: A Statistical Paradigm for Inferring Causality

Wu, P., Tang, W., Chen, T., He, H., Gunzler, D. D., & Tu, X. M. (2016) · ICSA book series in statistics · doi:10.1007/978-3-319-41259-7_1 · Cited by 1

Causal Inference Under the Potential Outcomes Approach

Mattei, A., & Mealli, F. (2025) · International Encyclopedia of Statistical Science · doi:10.1007/978-3-662-69359-9_96

Causal Inference

Gerring, J., & Christenson, D. P. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107775558.028 · Cited by 1

Diverse Tools of Causal Inference

Gerring, J., & Christenson, D. P. (2018) · Cambridge University Press eBooks · doi:10.1017/9781107775558.013

Causal Inference in the Social Sciences

Imbens, G. W. (2023) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-033121-114601 · Cited by 109