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ipt: a Python 3.7 package for causal inference by inverse probability tilting ----------------------------------------------------------------------------- by Bryan S. Graham, UC - Berkeley, e-mail: bgraham@econ.berkeley.edu This package includes a Python 3.6 implementation of the Average Treatment Effect of the Treated (ATT) estimator introduced in Graham, Pinto and Egel (2016). The function att() allows for sampling weights as well as "clustered standard errors", but these features have not yet been extensively tested. The package also includes a particular implementation of the E-estimator for the partially linear regression model due to Newey (1990) and Robins, Mark and Newey (1992). An implementation of the Average Treatment Effect (ATE) estimator introduced in Graham, Pinto and Egel (2012) is planned for a future update (as well as other causal inference estimation procedures). This package is offered "as is", without warranty, implicit or otherwise. While I would appreciate bug reports, suggestions for improvements and so on, I am unable to provide any meaningful user-support. Please e-mail me at bgraham@econ.berkeley.edu Please cite both the code and the underlying source articles listed below when using this code in your research. CODE CITATION --------------- Graham, Bryan S. (2017). "ipt: a Python 3.7 package for causal inference by inverse probability tilting," (Version 0.2.2) [Computer program]. Available at https://github.com/bryangraham/ipt (Accessed 04 Oct 2018) PAPER CITATIONS --------------- Graham, Bryan S., Cristine Pinto and Daniel Egel. (2012). “Inverse probability tilting for moment condition models with missing data,” Review of Economic Studies 79 (3): 1053 - 1079 Graham, Bryan S., Cristine Pinto and Daniel Egel. (2016). “Efficient estimation of data combination models by the method of auxiliary-to-study tilting (AST),” Journal of Business and Economic Statistics 31 (2): 288 - 301 Newey, Whitney. (1990). "Semiparametric efficiency bounds," Journal of Applied Econometrics 5 (2): 99 - 135 Robins, James M., Mark, Steven D. and Newey, Whitney K. (1992). "Estimating exposure effects by modelling the expectation of exposure conditional on confounders," Biometrics 48 (2): 479 - 495
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Tilting estimators for program evaluation for Python 3
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