Armstrong, T. and S. Shen (2015). Inference on optimal treatment assignments. Available at SSRN 2592479.
- Aronow, P. M. and C. Samii (2013). Estimating average causal effects under interference between units. arXiv preprint arXiv:1305.6156 3(4), 16.
Paper not yet in RePEc: Add citation now
- Aronow, P. M., C. Samii, et al. (2017). Estimating average causal effects under general interference, with application to a social network experiment. The Annals of Applied Statistics 11(4), 1912–1947.
Paper not yet in RePEc: Add citation now
Aruna, M. and M. R. Jyothirmayi (2011). The role of microfinance in women empowerment: A study on the shg bank linkage program in hyderabad (andhra pradesh).
- Athey, S. and G. Imbens (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences 113(27), 7353–7360.
Paper not yet in RePEc: Add citation now
Athey, S. and G. W. Imbens (2018). Design-based analysis in difference-in-differences settings with staggered adoption. Technical report, National Bureau of Economic Research.
Athey, S. and S. Wager (2017). Efficient policy learning. arXiv preprint arXiv:1702.02896.
Athey, S., D. Eckles, and G. W. Imbens (2018). Exact p-values for network interference. Journal of the American Statistical Association 113(521), 230–240.
Auerbach, E. (2019). Identification and estimation of a partially linear regression model using network data. arXiv preprint arXiv:1903.09679.
Baird, S., J. A. Bohren, C. McIntosh, and B. Özler (2018). Optimal design of experiments in the presence of interference. Review of Economics and Statistics 100(5), 844–860.
- Banerjee, A., A. G. Chandrasekhar, E. Duflo, and M. O. Jackson (2013). The diffusion of microfinance. Science 341(6144), 1236498.
Paper not yet in RePEc: Add citation now
Banerjee, A., A. G. Chandrasekhar, E. Duflo, and M. O. Jackson (2014). Gossip: Identifying central individuals in a social network. Technical report, National Bureau of Economic Research.
Belloni, A., V. Chernozhukov, and C. Hansen (2014). High-dimensional methods and inference on structural and treatment effects. Journal of Economic Perspectives 28(2), 29–50.
- Bertsimas, D. and J. Dunn (2017). Optimal classification trees. Machine Learning 106(7), 1039–1082.
Paper not yet in RePEc: Add citation now
Bhattacharya, D. (2009). Inferring optimal peer assignment from experimental data. Journal of the American Statistical Association 104(486), 486–500.
Bhattacharya, D. and P. Dupas (2012). Inferring welfare maximizing treatment assignment under budget constraints. Journal of Econometrics 167(1), 168–196.
Bhattacharya, D., P. Dupas, and S. Kanaya (2019). Demand and welfare analysis in discrete choice models with social interactions. Available at SSRN 3116716.
Bikhchandani, S., D. Hirshleifer, and I. Welch (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of political Economy 100(5), 992–1026.
Bond, R. M., C. J. Fariss, J. J. Jones, A. D. Kramer, C. Marlow, J. E. Settle, and J. H. Fowler (2012). A 61-million-person experiment in social influence and political mobilization. Nature 489(7415), 295.
- Boucheron, S., O. Bousquet, and G. Lugosi (2005). Theory of classification: A survey of some recent advances. ESAIM: probability and statistics 9, 323–375.
Paper not yet in RePEc: Add citation now
Bradic, J., S. Wager, and Y. Zhu (2019). Sparsity double robust inference of average treatment effects. arXiv preprint arXiv:1905.00744.
BramoulleÃŒÂ, Y., H. Djebbari, and B. Fortin (2009). Identification of peer effects through social networks. Journal of econometrics 150(1), 41–55.
- Brooks, R. L. (1941). On colouring the nodes of a network. In Mathematical Proceedings of the Cambridge Philosophical Society, Volume 37, pp. 194–197. Cambridge University Press.
Paper not yet in RePEc: Add citation now
Cai, J., A. De Janvry, and E. Sadoulet (2015). Social networks and the decision to insure. American Economic Journal: Applied Economics 7(2), 81–108.
- Campbell, M. J. and S. J. Walters (2014). How to design, analyse and report cluster randomised trials in medicine and health related research. John Wiley & Sons.
Paper not yet in RePEc: Add citation now
Carrell, S. E., B. I. Sacerdote, and J. E. West (2013). From natural variation to optimal policy? the importance of endogenous peer group formation. Econometrica 81(3), 855–882.
Chen, L.-Y. and S. Lee (2018). Best subset binary prediction. Journal of Econometrics 206(1), 39–56.
- Chernozhukov, V., D. Chetverikov, K. Kato, et al. (2014). Gaussian approximation of suprema of empirical processes. The Annals of Statistics 42(4), 1564–1597.
Paper not yet in RePEc: Add citation now
Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W. Newey, and J. Robins (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal 21(1), C1–C68.
Chernozhukov, V., W. K. Härdle, C. Huang, and W. Wang (2018). Lasso-driven inference in time and space. arXiv preprint arXiv:1806.05081.
Conley, T. G. and C. R. Udry (2010). Learning about a new technology: Pineapple in ghana. American economic review 100(1), 35–69.
- De Paula, A. (2017). Econometrics of network models. In Advances in Economics and Econometrics: Theory and Applications, Eleventh World Congress, pp. 268–323. Cambridge University Press Cambridge.
Paper not yet in RePEc: Add citation now
Dehejia, R. H. (2005). Program evaluation as a decision problem. Journal of Econometrics 125(1-2), 141–173.
- Devroye, L., L. Györfi, and G. Lugosi (2013). A probabilistic theory of pattern recognition, Volume 31. Springer Science & Business Media.
Paper not yet in RePEc: Add citation now
- Dimakopoulou, M., S. Athey, and G. Imbens (2017). Estimation considerations in contextual bandits. arXiv preprint arXiv:1711.07077.
Paper not yet in RePEc: Add citation now
- Domingos, P. and M. Richardson (2001). Mining the network value of customers. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 57–66. ACM.
Paper not yet in RePEc: Add citation now
Ductor, L., M. Fafchamps, S. Goyal, and M. J. van der Leij (2014). Social networks and research output. Review of Economics and Statistics 96(5), 936–948.
- Dudik, M., J. Langford, and L. Li (2011). Doubly robust policy evaluation and learning. arXiv preprint arXiv:1103.4601.
Paper not yet in RePEc: Add citation now
Duflo, E. and E. Saez (2003). The role of information and social interactions in retirement plan decisions: Evidence from a randomized experiment. The Quarterly journal of economics 118(3), 815–842.
Dupas, P. (2014). Short-run subsidies and long-run adoption of new health products: Evidence from a field experiment. Econometrica 82(1), 197–228.
Dupas, P. and J. Robinson (2013). Savings constraints and microenterprise development: Evidence from a field experiment in kenya. American Economic Journal: Applied Economics 5(1), 163–92.
Elliott, G. and R. P. Lieli (2013). Predicting binary outcomes. Journal of Econometrics 174(1), 15–26.
Farrell, M. H. (2015). Robust inference on average treatment effects with possibly more covariates than observations. Journal of Econometrics 189(1), 1–23.
Farrell, M. H., T. Liang, and S. Misra (2018). Deep neural networks for estimation and inference: Application to causal effects and other semiparametric estimands.
- Forastiere, L., E. M. Airoldi, and F. Mealli (2016). Identification and estimation of treatment and interference effects in observational studies on networks. arXiv preprint arXiv:1609.06245.
Paper not yet in RePEc: Add citation now
Galeotti, A. and S. Goyal (2009). Influencing the influencers: a theory of strategic diffusion. The RAND Journal of Economics 40(3), 509–532.
Galeotti, A., B. Golub, and S. Goyal (2017). Targeting interventions in networks.
Goldsmith-Pinkham, P. and G. W. Imbens (2013). Social networks and the identification of peer effects. Journal of Business & Economic Statistics 31(3), 253–264.
- Granovetter, M. (1978). Threshold models of collective behavior. American journal of sociology 83(6), 1420–1443.
Paper not yet in RePEc: Add citation now
- He, X. and K. Song (2018). Measuring diffusion over a large network. arXiv preprint arXiv:1812.04195.
Paper not yet in RePEc: Add citation now
Hirano, K. and J. R. Porter (2009). Asymptotics for statistical treatment rules.
- Horvitz, D. G. and D. J. Thompson (1952). A generalization of sampling without replacement from a finite universe. Journal of the American statistical Association 47(260), 663–685.
Paper not yet in RePEc: Add citation now
- Imbens, G. W. (2000). The role of the propensity score in estimating dose-response functions. Biometrika 87(3), 706–710.
Paper not yet in RePEc: Add citation now
Imbens, G. W. and D. B. Rubin (2015). Causal inference in statistics, social, and biomedical sciences. Cambridge University Press.
- Jackson, M. O. and E. Storms (2018). Behavioral communities and the atomic structure of networks. Available at SSRN 3049748.
Paper not yet in RePEc: Add citation now
- Janson, S. (2004). Large deviations for sums of partly dependent random variables. Random Structures & Algorithms 24(3), 234–248.
Paper not yet in RePEc: Add citation now
Jones, J. J., R. M. Bond, E. Bakshy, D. Eckles, and J. H. Fowler (2017). Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 us presidential election. PloS one 12(4), e0173851.
- Kallus, N. (2017). Recursive partitioning for personalization using observational data. In Proceedings of the 34th International Conference on Machine LearningVolume 70, pp. 1789–1798. JMLR. org.
Paper not yet in RePEc: Add citation now
- Kempe, D., J. Kleinberg, and EÃŒÂ. Tardos (2003). Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 137–146. ACM.
Paper not yet in RePEc: Add citation now
- Kermack, W. O. and A. G. McKendrick (1927). A contribution to the mathematical theory of epidemics. Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character 115(772), 700–721.
Paper not yet in RePEc: Add citation now
- Kim, D. A., A. R. Hwong, D. Stafford, D. A. Hughes, A. J. O’Malley, J. H. Fowler, and N. A. Christakis (2015). Social network targeting to maximise population behaviour change: a cluster randomised controlled trial. The Lancet 386(9989), 145–153.
Paper not yet in RePEc: Add citation now
Kitagawa, T. and A. Tetenov (2017). Equality-minded treatment choice. Technical report, cemmap working paper.
Kitagawa, T. and A. Tetenov (2018). Who should be treated? Empirical welfare maximization methods for treatment choice. Econometrica 86(2), 591–616.
Kremer, I., Y. Mansour, and M. Perry (2014). Implementing the “wisdom of the crowdâ€Â. Journal of Political Economy 122(5), 988–1012.
- Leskovec, J., L. A. Adamic, and B. A. Huberman (2007). The dynamics of viral marketing. ACM Transactions on the Web (TWEB) 1(1), 5.
Paper not yet in RePEc: Add citation now
Leung, M. P. (2019). Treatment and spillover effects under network interference. Available at SSRN 2757313.
- Liu, L., M. G. Hudgens, B. Saul, J. D. Clemens, M. Ali, and M. E. Emch (2019). Doubly robust estimation in observational studies with partial interference. Stat 8(1), e214.
Paper not yet in RePEc: Add citation now
- Lu, C., B. Schölkopf, and J. M. HernaÃŒÂndez-Lobato (2018). Deconfounding reinforcement learning in observational settings. arXiv preprint arXiv:1812.10576.
Paper not yet in RePEc: Add citation now
- Luedtke, A. R. and M. J. Van Der Laan (2016). Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy. Annals of statistics 44(2), 713.
Paper not yet in RePEc: Add citation now
- Manresa, E. (2013). Estimating the structure of social interactions using panel data. Unpublished Manuscript. CEMFI, Madrid.
Paper not yet in RePEc: Add citation now
Manski (2004). Statistical treatment rules for heterogeneous populations. Econometrica 72(4), 1221–1246.
Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. The review of economic studies 60(3), 531–542.
Manski, C. F. (2013). Identification of treatment response with social interactions. The Econometrics Journal 16(1), S1–S23.
Mbakop, E. and M. Tabord-Meehan (2016). Model selection for treatment choice: Penalized welfare maximization. arXiv preprint arXiv:1609.03167.
Muralidharan, K., P. Niehaus, and S. Sukhtankar (2017). General equilibrium effects of (improving) public employment programs: Experimental evidence from india. Technical report, National Bureau of Economic Research.
Murphy, S. A. (2003). Optimal dynamic treatment regimes. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65(2), 331–355.
- Negahban, S. N., P. Ravikumar, M. J. Wainwright, B. Yu, et al. (2012). A unified framework for high-dimensional analysis of m-estimators with decomposable regularizers. Statistical Science 27(4), 538–557.
Paper not yet in RePEc: Add citation now
- Nie, X., E. Brunskill, and S. Wager (2019). Learning when-to-treat policies. arXiv preprint arXiv:1905.09751.
Paper not yet in RePEc: Add citation now
- Ogburn, E. L., O. Sofrygin, I. Diaz, and M. J. van der Laan (2017). Causal inference for social network data. arXiv preprint arXiv:1705.08527.
Paper not yet in RePEc: Add citation now
- Opper, I. M. (2016). Does helping john help sue? evidence of spillovers in education.
Paper not yet in RePEc: Add citation now
- Paluck, E. L., H. Shepherd, and P. M. Aronow (2016). Changing climates of conflict: A social network experiment in 56 schools. Proceedings of the National Academy of Sciences 113(3), 566–571.
Paper not yet in RePEc: Add citation now
- Pollard, D. (1990). Empirical processes: theory and applications. In NSF-CBMS regional conference series in probability and statistics, pp. i–86. JSTOR.
Paper not yet in RePEc: Add citation now
- Qian, M. and S. A. Murphy (2011). Performance guarantees for individualized treatment rules. Annals of statistics 39(2), 1180.
Paper not yet in RePEc: Add citation now
- Robins, J. M., A. Rotnitzky, and L. P. Zhao (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American statistical Association 89(427), 846–866.
Paper not yet in RePEc: Add citation now
- Rubin, D. B. (1990). Formal mode of statistical inference for causal effects. Journal of statistical planning and inference 25(3), 279–292.
Paper not yet in RePEc: Add citation now
Stoye, J. (2009). Minimax regret treatment choice with finite samples. Journal of Econometrics 151(1), 70–81.
Stoye, J. (2012). Minimax regret treatment choice with covariates or with limited validity of experiments. Journal of Econometrics 166(1), 138–156.
- Su, L., W. Lu, and R. Song (2019). Modelling and estimation for optimal treatment decision with interference. Stat 8(1), e219.
Paper not yet in RePEc: Add citation now
Tetenov, A. (2012). Statistical treatment choice based on asymmetric minimax regret criteria. Journal of Econometrics 166(1), 157–165.
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58(1), 267–288.
Paper not yet in RePEc: Add citation now
- Tibshirani, R., M. Wainwright, and T. Hastie (2015). Statistical learning with sparsity: the lasso and generalizations. Chapman and Hall/CRC.
Paper not yet in RePEc: Add citation now
- Valente, T. W., A. Ritt-Olson, A. Stacy, J. B. Unger, J. Okamoto, and S. Sussman (2007). Peer acceleration: effects of a social network tailored substance abuse prevention program among high-risk adolescents. Addiction 102(11), 1804–1815.
Paper not yet in RePEc: Add citation now
- van der Laan, M. J. (2012). Causal inference for networks. Working Paper Series, UC Berkeley.
Paper not yet in RePEc: Add citation now
- Van Der Vaart, A. W. and J. A. Wellner (1996). Weak convergence. In Weak convergence and empirical processes, pp. 16–28. Springer.
Paper not yet in RePEc: Add citation now
- Vazquez-Bare, G. (2017). Identification and estimation of spillover effects in randomized experiments. arXiv preprint arXiv:1711.02745.
Paper not yet in RePEc: Add citation now
- Wager, S. and K. Xu (2019). Experimenting in equilibrium. arXiv preprint arXiv:1903.02124.
Paper not yet in RePEc: Add citation now
Wager, S. and S. Athey (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association 113(523), 1228–1242.
- Wainwright, M. J. (2019). High-dimensional statistics: A non-asymptotic viewpoint, Volume 48. Cambridge University Press.
Paper not yet in RePEc: Add citation now
Weaver, J. (2016). Jobs for sale: Corruption and misallocation in hiring. Technical report, Tech. rep., Mimeo, Yale University. B. Treatment. 1.
- Wenocur, R. S. and R. M. Dudley (1981). Some special vapnik-chervonenkis classes. Discrete Mathematics 33(3), 313–318.
Paper not yet in RePEc: Add citation now
Wittenberg, E., G. A. Ritter, and L. A. Prosser (2013). Evidence of spillover of illness among household members: Eq-5d scores from a us sample. Medical Decision Making 33(2), 235–243.
Zhang, B., A. A. Tsiatis, E. B. Laber, and M. Davidian (2012). A robust method for estimating optimal treatment regimes. Biometrics 68(4), 1010–1018.
Zhou, X., N. Mayer-Hamblett, U. Khan, and M. R. Kosorok (2017). Residual weighted learning for estimating individualized treatment rules. Journal of the American Statistical Association 112(517), 169–187.
Zhou, Z., S. Athey, and S. Wager (2018). Offline multi-action policy learning: Generalization and optimization. arXiv preprint arXiv:1810.04778.
Zubcsek, P. P. and M. Sarvary (2011). Advertising to a social network. Quantitative Marketing and Economics 9(1), 71–107.