Estimating average causal effects under general interference, with application to a social network experiment
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This paper presents a randomization-based framework for estimating causal effects under interference between units motivated by challenges that arise in analyzing experiments on social networks. The framework integrates three components: (i) an experimental design that defines the probability distribution of treatment assignments, (ii) a mapping that relates experimental treatment assignments to exposures received by units in the experiment, and (iii) estimands that make use of the experiment to answer questions of substantive interest. We develop the case of estimating average unit-level causal effects from a randomized experiment with interference of arbitrary but known form. The resulting estimators are based on inverse probability weighting. We provide randomization-based variance estimators that account for the complex clustering that can occur when interference is present. We also establish consistency and asymptotic normality under local dependence assumptions. We discuss refinements including covariate-adjusted effect estimators and ratio estimation. We evaluate empirical performance in realistic settings with a naturalistic simulation using social network data from American schools. We then present results from a field experiment on the spread of anti-conflict norms and behavior among school students.
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Cites
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293 · see citing works
Recent Developments in the Econometrics of Program Evaluation
Imbens, G. W., & Wooldridge, J. M. (2009) · Journal of Economic Literature · doi:10.1257/jel.47.1.5 · Cited by 4,926 · see citing works
A generalization of sampling without replacement from a finite universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483446 · Cited by 4,293 · see citing works
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210 · see citing works
Model Assisted Survey Sampling
Särndal, C., Swensson, B., & Wretman, J. H. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-4378-6 · Cited by 3,417 · see citing works
Bayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344064 · Cited by 2,503 · see citing works
MacKinnon, J. G., & White, H. (1985) · Journal of Econometrics · doi:10.1016/0304-4076(85)90158-7 · Cited by 1,879 · see citing works
The role of the propensity score in estimating dose-response functions
Imbens, G. W. (2000) · Biometrika · doi:10.1093/biomet/87.3.706 · Cited by 1,747 · see citing works
Empirical Strategies in Labor Economics
Angrist, J. D., & Krueger, A. B. (1999) · Handbook of Labor Economics · doi:10.1016/s1573-4463(99)03004-7 · Cited by 1,628 · see citing works
On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
Neyman, J., Dabrowska, D. M., & Speed, T. P. (1990) · Statistical Science · doi:10.1214/ss/1177012031 · Cited by 1,616 · see citing works
Identification of Peer Effects through Social Networks
Bramoullé, Y., Djebbari, H., & Fortin, B. (2009) · Social Science Research Network · doi:10.1016/j.jeconom.2008.12.021 · Cited by 1,388 · see citing works
Aral, S., & Walker, D. (2011) · Management Science · doi:10.1287/mnsc.1110.1421 · Cited by 786 · see citing works
Goodreau, S. M., Kitts, J. A., & Morris, M. (2009) · Demography · doi:10.1353/dem.0.0045 · Cited by 776 · see citing works
Toward Causal Inference With Interference
Hudgens, M. G., & Halloran, M. E. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000292 · Cited by 768 · see citing works
Model-Based Clustering for Social Networks
Handcock, M. S., Raftery, A. E., & Tantrum, J. M. (2007) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2007.00471.x · Cited by 759 · see citing works
Goodness of Fit of Social Network Models
Hunter, D. R., Goodreau, S. M., & Handcock, M. S. (2008) · Journal of the American Statistical Association · doi:10.1198/016214507000000446 · Cited by 689 · see citing works
Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique
Lin, W. T. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas583 · Cited by 633 · see citing works
Changing climates of conflict: A social network experiment in 56 schools
Paluck, E. L., Shepherd, H., & Aronow, P. M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1514483113 · Cited by 610 · see citing works
Self-citationFormal mode of statistical inference for causal effects
Rubin, D. B. (1990) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(90)90077-8 · Cited by 483 · see citing works
Sobel, M. E. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000636 · Cited by 468 · see citing works
Survey Design under the Regression Superpopulation Model
Isaki, C. T., & Fuller, W. A. (1982) · Journal of the American Statistical Association · doi:10.1080/01621459.1982.10477770 · Cited by 423 · see citing works
On causal inference in the presence of interference
Tchetgen, E. J. T., & VanderWeele, T. J. (2010) · Statistical Methods in Medical Research · doi:10.1177/0962280210386779 · Cited by 420 · see citing works
The Consistency Statement in Causal Inference
Cole, S. R., & Frangakis, C. (2008) · Epidemiology · doi:10.1097/ede.0b013e31818ef366 · Cited by 413 · see citing works
Assessing respondent-driven sampling
Goel, S., & Salganik, M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000261107 · Cited by 387 · see citing works
Interference Between Units in Randomized Experiments
Rosenbaum, P. R. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001112 · Cited by 371 · see citing works
Concerning the Consistency Assumption in Causal Inference
VanderWeele, T. J. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181bd5638 · Cited by 368 · see citing works
Advances in exponential random graph (p*) models applied to a large social network
Goodreau, S. M. (2006) · Social Networks · doi:10.1016/j.socnet.2006.08.001 · Cited by 342 · see citing works
Identification of treatment response with social interactions
Manski, C. F. (2013) · Econometrics Journal · doi:10.1111/j.1368-423x.2012.00368.x · Cited by 283 · see citing works
Fuller, W. A. (2009) · Wiley eBooks · doi:10.1002/9780470523551 · Cited by 237 · see citing works
Hartley, H. O., & Ross, A. (1954) · Nature · doi:10.1038/174270a0 · Cited by 231 · see citing works
Normal approximation under local dependence
Chen, L. H. Y., & Shao, Q.-M. (2004) · The Annals of Probability · doi:10.1214/009117904000000450 · Cited by 217 · see citing works
Peer pressure against prejudice: A high school field experiment examining social network change
Paluck, E. L. (2010) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2010.11.017 · Cited by 194 · see citing works
Graph cluster randomization: network exposure to multiple universes
Ugander, J., Karrer, B., Backstrom, L., & Kleinberg, J. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487695 · Cited by 183 · see citing works
Causal Diagrams for Interference
Ogburn, E. L., & VanderWeele, T. J. (2014) · Statistical Science · doi:10.1214/14-sts501 · Cited by 137 · see citing works
Large Sample Randomization Inference of Causal Effects in the Presence of Interference
Liu, L., & Hudgens, M. G. (2013) · Journal of the American Statistical Association · doi:10.1080/01621459.2013.844698 · Cited by 133 · see citing works
Cluster–Robust Variance Estimation for Dyadic Data
Aronow, P. M., Samii, C., & Assenova, V. (2015) · Political Analysis · doi:10.1093/pan/mpv018 · Cited by 128 · see citing works
Self-citationReasoning about Interference Between Units: A General Framework
Bowers, J., Fredrickson, M., & Panagopoulos, C. (2012) · Political Analysis · doi:10.1093/pan/mps038 · Cited by 124 · see citing works
A Class of Robust Sampling Designs for Large-Scale Surveys
Brewer, K. R. W. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10481053 · Cited by 122 · see citing works
An Essay on the Logical Foundations of Survey Sampling, Part One*
Basu, D. (2010) · Springer eBooks · doi:10.1007/978-1-4419-5825-9_24 · Cited by 95 · see citing works
Fattorini, L. (2006) · Biometrika · doi:10.1093/biomet/93.2.269 · Cited by 91 · see citing works
Estimation of Causal Peer Influence Effects
Toulis, P., & Kao, E. K. (2013) · Cited by 87 · see citing works
On a Method of Using Multi-Auxiliary Information in Sample Surveys
Raj, D. (1965) · Journal of the American Statistical Association · doi:10.1080/01621459.1965.10480789 · Cited by 84 · see citing works
Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments
Middleton, J. A., & Aronow, P. M. (2015) · Statistics Politics and Policy · doi:10.1515/spp-2013-0002 · Cited by 74 · see citing works
Self-citationA Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments
Aronow, P. M., & Middleton, J. A. (2013) · Journal of Causal Inference · doi:10.1515/jci-2012-0009 · Cited by 72 · see citing works
Self-citationDesigning Experiments to Measure Spillover Effects
Baird, S., Bohren, J. A., McIntosh, C., & Özler, B. (2014) · World Bank, Washington, DC eBooks · doi:10.1596/1813-9450-6824 · Cited by 51 · see citing works
Design and Analysis of Experiments in Networks: Reducing Bias from Interference
Eckles, D., Karrer, B., & Ugander, J. (2017) · Journal of Causal Inference · doi:10.1515/jci-2015-0021 · Cited by 49 · see citing works
Graph cluster randomization: network exposure to multiple universesPreprint
Ugander, J. H., Karrer, B., Bäckström, L., & Kleinberg, J. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.6979 · Cited by 41 · see citing works
Latent transition analysis: Inference and estimation
Chung, H., Lanza, S. T., & Loken, E. (2007) · Statistics in Medicine · doi:10.1002/sim.3130 · Cited by 38 · see citing works
Rosenbaum, P. R. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00560.x · Cited by 35 · see citing works
On the Convergence of the Horvitz‐Thompson Estimator
Robinson, P. M. (1982) · Australian Journal of Statistics · doi:10.1111/j.1467-842x.1982.tb00829.x · Cited by 26 · see citing works
On the covariance between related Horvitz-Thompson estimators
Wood, J. B. (2008) · Journal of Official Statistics · Cited by 23 · see citing works
Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized ExperimentsPreprint
Middleton, J. A., & Aronow, P. M. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1803849 · Cited by 23 · see citing works
Self-citationConservative variance estimation for sampling designs with zero pairwise inclusion probabilities
Aronow, P. M., & Samii, C. (2012) · Survey methodology · Cited by 10 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 4
- ARONOW, P. M. (2013). Model Assisted Causal Inference. Ph.D. thesis, Dept. Political Science, Yale Univ., New Haven, CT.
- BASU, D. (1971). An essay on the logical foundations of survey sampling. I. In Foundations of Statistical Inference (V. Godambe and D. A. Sprott, eds.) 203-242.
- COX, D. R. (1958). Planning of Experiments. Wiley, New York.
- MANSKI, C. F. (1995). Identification Problems in the Social Sciences. Harvard Univ. Press, Cam- bridge, MA.
Cited by
Sorted by citations
The Econometrics of Randomized Experiments
Athey, S. C., & Imbens, G. W. (2017) · Handbook of economic field experiments · doi:10.1016/bs.hefe.2016.10.003 · Cited by 561 · 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
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
Exactp-Values for Network Interference
Athey, S. C., Eckles, D., & Imbens, G. W. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1241178 · Cited by 185 · see citing works
Peer Effects in Networks: A Survey
Bramoullé, Y., Djebbari, H., & Fortin, B. (2020) · Annual Review of Economics · doi:10.1146/annurev-economics-020320-033926 · Cited by 181 · see citing works
Nonrandom Exposure to Exogenous Shocks
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Forastiere, L., Airoldi, E. M., & Mealli, F. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1768100 · Cited by 144 · see citing works
Kuang, K., Li, L., Geng, Z., Xu, L., Zhang, K., Liao, B., Huang, H., Ding, P., Miao, W., & Jiang, Z. (2020) · Engineering · doi:10.1016/j.eng.2019.08.016 · Cited by 133 · see citing works
Average treatment effects in the presence of unknown interference
Sävje, F., Aronow, P. M., & Hudgens, M. G. (2021) · The Annals of Statistics · doi:10.1214/20-aos1973 · Cited by 133 · see citing works
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Brand, J. E., Zhou, X., & Xie, Y. F. (2023) · Annual Review of Sociology · doi:10.1146/annurev-soc-030420-015345 · Cited by 124 · see citing works
Blattman, C., GREEN, D. P., Ortega, D., & Tobón, S. (2021) · Journal of the European Economic Association · doi:10.1093/jeea/jvab002 · Cited by 122 · see citing works
Analyzing Two-Stage Experiments in the Presence of Interference
Basse, G., & Feller, A. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1323641 · Cited by 88 · see citing works
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BackgroundDesign and Analysis of Switchback Experiments
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Wager, S., & Xu, K. (2021) · Management Science · doi:10.1287/mnsc.2020.3844 · Cited by 54 · see citing works
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Causal analysis of central bank holdings of corporate bonds under interference
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Model-assisted design of experiments in the presence of network-correlated outcomes
Basse, G. W., & Airoldi, E. (2015) · Biometrika · doi:10.1093/biomet/asy036 · Cited by 90 · see citing works
Blattman, C., GREEN, D. P., Ortega, D., & Tobón, S. (2017) · National Bureau of Economic Research · doi:10.3386/w23941 · Cited by 53 · see citing works
Causal models for longitudinal and panel data: a survey
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Random graph asymptotics for treatment effect estimation under network interference
Li, S., & Wager, S. (2022) · The Annals of Statistics · doi:10.1214/22-aos2191 · Cited by 47 · see citing works
Reducing gunshot victimization in high-risk social networks through direct and spillover effects
Wood, G., & Papachristos, A. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0688-1 · Cited by 11 · see citing works
Bipartite Causal Inference with Interference
Zigler, C. M., & Papadogeorgou, G. (2020) · Statistical Science · doi:10.1214/19-sts749 · Cited by 42 · see citing works
Salimi, B., Parikh, H., Kayali, M., Getoor, L., Roy, S., & Suciu, D. (2020) · SIGMOD Conference · doi:10.1145/3318464.3389759 · Cited by 55 · see citing works
Little evidence that military policing reduces crime or improves human security
Blair, R., & WEINTRAUB, M. I. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01600-1 · Cited by 40 · see citing works
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Ariel, B., Sutherland, A., & Sherman, L. W. (2019) · Journal of Experimental Criminology · doi:10.1007/s11292-018-9344-4 · Cited by 38 · see citing works
Limit theorems for network dependent random variables
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Jo, W., Sunder, S., Choi, J., & Trivedi, M. (2020) · Marketing Science · doi:10.1287/mksc.2019.1174 · Cited by 38 · see citing works
Langen, H., & Huber, M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0278937 · Cited by 37 · see citing works
Basse, G. W., & Airoldi, E. (2017) · Sociological methodology · doi:10.1177/0081175018782569 · Cited by 64 · see citing works
Generalized Propensity Score Approach to Causal Inference with Spatial Interference
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The Mobility Effects Hypothesis: Past, Present, and Future
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Zelizer, A. (2019) · American Political Science Review · doi:10.1017/s0003055419000078 · Cited by 19 · see citing works
Regression Adjustments for Estimating the Global Treatment Effect in Experiments with Interference
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A weak law for moments of pairwise stable networks
Leung, M. P. (2019) · Journal of Econometrics · doi:10.1016/j.jeconom.2019.01.010 · Cited by 31 · see citing works
Average direct and indirect causal effects under interference
Hu, Y., Li, S., & Wager, S. (2022) · Biometrika · doi:10.1093/biomet/asac008 · Cited by 31 · see citing works
Disentangling causality: assumptions in causal discovery and inference
Vonk, M., Malekovic, N., Bäck, T., & Kononova, A. V. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10411-9 · Cited by 30 · see citing works
Designs for estimating the treatment effect in networks with interference
Jagadeesan, R., Pillai, N. S., & Volfovsky, A. (2017) · Annals of Statistics · doi:10.1214/18-aos1807 · Cited by 57 · see citing works
Online Experimentation: Benefits, Operational and Methodological Challenges, and Scaling Guide
Bojinov, I., & Gupta, S. (2022) · Harvard Data Science Review · doi:10.1162/99608f92.a579756e · Cited by 28 · see citing works
Causal inference with misspecified exposure mappings: separating definitions and assumptions
Sävje, F. (2023) · Biometrika · doi:10.1093/biomet/asad019 · Cited by 28 · see citing works
Randomized Experiments to Detect and Estimate Social Influence in Networks
Taylor, S. J., & Eckles, D. (2018) · Computational social sciences · doi:10.1007/978-3-319-77332-2_16 · Cited by 28 · see citing works
Identification of Spatial Spillovers: Do's and Don'ts
Debarsy, N., & Gallo, J. L. (2025) · Journal of Economic Surveys · doi:10.1111/joes.12692 · Cited by 29 · see citing works
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Harshaw, C., Sävje, F., Spielman, D. A., & Zhang, P. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2285474 · Cited by 25 · see citing works
Randomized graph cluster randomization
Ugander, J. H., & Yin, H. (2023) · Journal of Causal Inference · doi:10.1515/jci-2022-0014 · Cited by 24 · see citing works
Permutation and randomization tests for network analysis
Fredrickson, M., & Chen, Y. (2019) · Social Networks · doi:10.1016/j.socnet.2019.08.001 · Cited by 23 · see citing works
Kang, H., & Imbens, G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.04464 · Cited by 36 · see citing works
Spillover Effects in Experimental Data
Aronow, P. M., Eckles, D., Samii, C., & Zonszein, S. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108777919.021 · Cited by 23 · see citing works
Self-citationA Graph-Theoretic Approach to Randomization Tests of Causal Effects under General Interference
Puelz, D., Basse, G., Feller, A., & Toulis, P. (2021) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12478 · Cited by 22 · see citing works
A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R.
Saul, B., & Hudgens, M. G. (2017) · Journal of Statistical Software · doi:10.18637/jss.v082.i02 · Cited by 22 · see citing works
Elements of estimation theory for causal effects in the presence of network interferencePreprint
Sussman, D. L., & Airoldi, E. M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.03578 · Cited by 22 · see citing works
Policy Targeting under Network Interference
Viviano, D. (2024) · The Review of Economic Studies · doi:10.1093/restud/rdae041 · Cited by 20 · see citing works
Who should get vaccinated? Individualized allocation of vaccines over SIR network
Kitagawa, T., & Wang, G. (2021) · Journal of Econometrics · doi:10.1016/j.jeconom.2021.09.009 · Cited by 20 · see citing works
Chen, Z.-Y., Fan, Z., & Sun, M. (2018) · European Journal of Operational Research · doi:10.1016/j.ejor.2018.09.025 · Cited by 20 · see citing works
Randomization Inference for Peer Effects
Li, X., Ding, P., Lin, Q., Yang, D., & Liu, J. S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1512863 · Cited by 20 · see citing works
The Design of Clustered Observational Studies in Education
Page, L. C., Lenard, M., & Keele, L. J. (2020) · AERA Open · doi:10.1177/2332858420954401 · Cited by 20 · see citing works
A Network Formation Model Based on Subgraphs
Chandrasekhar, A. G., & Jackson, M. O. (2025) · The Review of Economic Studies · doi:10.1093/restud/rdaf013 · Cited by 64 · see citing works
Network Experimentation at Scale
Karrer, B., Shi, L., Bhole, M., Goldman, M., Palmer, T., Gelman, C., Konutgan, M., & Sun, F. J. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467091 · Cited by 20 · see citing works
Channels for Influence or Maps of Behavior? A Field Experiment on Social Networks and Cooperation
Atwell, P. C., & Nathan, N. L. (2021) · American Journal of Political Science · doi:10.1111/ajps.12586 · Cited by 16 · see citing works
Estimating the total treatment effect in randomized experiments with unknown network structure
Yu, C. L., Airoldi, E. M., Borgs, C., & Chayes, J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2208975119 · Cited by 19 · see citing works
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 18 · see citing works
Zhang, Y., & Zhao, Q. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2199814 · Cited by 18 · see citing works
Causal Inference with Networked Treatment Diffusion
An, W. (2018) · Sociological Methodology · doi:10.1177/0081175018785216 · Cited by 18 · see citing works
Larson, J., & Lewis, J. I. (2024) · American Political Science Review · doi:10.1017/s0003055424000303 · Cited by 17 · see citing works
Multi-armed Bandit Experimental Design: Online Decision-Making and Adaptive Inference
Simchi‐Levi, D., & Wang, C. (2024) · Management Science · doi:10.1287/mnsc.2023.00492 · Cited by 17 · see citing works
Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests
Yuan, Y., Altenburger, K. M., & Kooti, F. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 17 · see citing works
Randomization and The Pernicious Effects of Limited Budgets on Auction ExperimentsPreprint
Basse, G., Soufiani, H. A., & Lambert, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.09171 · Cited by 17 · see citing works
Causal Models for Longitudinal and Panel Data: A Survey
Arkhangelsky, D., & Imbens, G. W. (2023) · National Bureau of Economic Research · doi:10.3386/w31942 · Cited by 20 · see citing works
Blattman, C., Green, D. P., Ortega, D., & Tobon, S. (2017) · doi:10.2139/ssrn.3050823 · Cited by 29 · see citing works
Spillover Effects in the Presence of Unobserved Networks
Egami, N. (2020) · Political Analysis · doi:10.1017/pan.2020.28 · Cited by 16 · see citing works
Rate-optimal cluster-randomized designs for spatial interference
Leung, M. P. (2022) · The Annals of Statistics · doi:10.1214/22-aos2224 · Cited by 16 · see citing works
Keele, L. J., & Titiunik, R. (2018) · CESifo Economic Studies · doi:10.1093/cesifo/ify004 · Cited by 16 · see citing works
Estimating social influence in a social network using potential outcomes.
Loh, W. W., & Ren, D. (2020) · Psychological Methods · doi:10.1037/met0000356 · Cited by 16 · see citing works
Spillover Effects in the Presence of Unobserved Networks
Egami, N. (2021) · RePEc: Research Papers in Economics · Cited by 16 · see citing works
Central limit theorems via Stein's method for randomized experiments under interferencePreprint
Chin, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.03105 · Cited by 16 · see citing works
Li, H., Rosete, S., Coyle, J., Phillips, R. V., Hejazi, N. S., Malenica, I., Arnold, B. F., Benjamin‐Chung, J., Mertens, A., Colford, J. M., et al. (2022) · Statistics in Medicine · doi:10.1002/sim.9348 · Cited by 15 · see citing works
Design-based identification with formula instruments: a review
Константинович, Б. К., Hull, P., & Jaravel, X. (2024) · Econometrics Journal · doi:10.1093/ectj/utae003 · Cited by 15 · see citing works
Keller, B., & Branson, Z. (2024) · Asia Pacific Education Review · doi:10.1007/s12564-024-09957-2 · Cited by 18 · see citing works
Efficient semiparametric estimation of network treatment effects under partial interference
Park, C., & Kang, H. (2022) · Biometrika · doi:10.1093/biomet/asac009 · Cited by 15 · see citing works
A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications
Reich, B. J., Yang, S., Guan, Y., Giffin, A., Miller, M. J., & Rappold, A. G. (2021) · International Statistical Review · doi:10.1111/insr.12452 · Cited by 15 · see citing works
Treatment Effects in Market EquilibriumPreprint
Munro, E., Xu, K., & Wager, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.11647 · Cited by 15 · see citing works
Jiang, Z., Imai, K., & Malani, A. (2022) · Biometrics · doi:10.1111/biom.13782 · Cited by 14 · see citing works
Adjusting for Peer-Influence in Propensity Scoring When Estimating Treatment EffectsPreprint
Jackson, M. O., Lin, Z., & Yu, N. N. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3522256 · Cited by 11 · see citing works
Causal Inference with Bipartite DesignsPreprint
Doudchenko, N., Zhang, M., Drynkin, E., Airoldi, E. M., Mirrokni, V., & Pouget-Abadie, J. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3757188 · Cited by 14 · see citing works
Design and Analysis of Switchback ExperimentsPreprint
Bojinov, I., Simchi‐Levi, D., & Zhao, J. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3684168 · Cited by 14 · see citing works
Design and analysis of bipartite experiments under a linear exposure-response model
Harshaw, C., Sävje, F., Eisenstat, D., Mirrokni, V., & Pouget-Abadie, J. (2023) · Electronic Journal of Statistics · doi:10.1214/23-ejs2111 · Cited by 13 · see citing works
Indirect Effects of Access to Finance
Cai, J., & Szeidl, Á. (2022) · American Economic Review · doi:10.1257/aer.20220711 · Cited by 15 · see citing works
- Authors
- Aronow, P. M., & Samii, C.
- Affiliations
- Yale University (US)
- Venue
- The Annals of Applied Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2017-12-01
- Volume
- 11
- Issue
- 4
- Pages
- 1912-1947
- ISSN
- 1932-6157 (ISSN-L)
- 1941-7330
- OpenAlex
- W2735728227
- Cited by
- 347
- References
- 51
- Citations by year
- 2026: 47 · 2025: 47 · 2024: 35 · 2023: 47 · 2022: 30 · 2021: 46 · 2020: 39 · 2019: 21 · 2018: 20 · 2017: 10 · 2016: 4 · 2015: 1
- FWCI
- 17.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Annals of Applied Statistics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- randomization inference, inverse probability weighting, variance estimation, local dependence, covariate adjustment, ratio estimation, school students
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.
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