pstest-package: pstest: An R Package for assessing the goodness-of-fit of...

pstest-packageR Documentation

pstest: An R Package for assessing the goodness-of-fit of parametric propensity score models.

Description

The propensity score is one of the most widely used tools in studying the causal effect of a treatment, intervention, or policy. Given that the propensity score is usually unknown, it has to be estimated, implying that the reliability of many treatment effect estimators depends on the correct specification of the (parametric) propensity score. This package provides data-driven nonparametric diagnostic tools for detecting propensity score misspecification.

Details

This R package implements the class of specification test for the propensity score proposed in Sant'Anna and Song (2016), ‘Specification Tests for the Propensity Score’, available at Pedro H.C. Sant'Anna webpage, http://sites.google.com/site/pedrohcsantanna/.

In short, this package implements Kolmogorov-Smirnov and Cramer-von Mises type tests for parametric propensity score models with either logistic ('logit'), or normal ('probit') link function. Critical values are computed with the assistance of a multiplier bootstrap.

The tests are based on the integrated conditional moment approach, where the weight function used is based on an orthogonal projection onto the tangent space of nuisance parameters. As a result, the tests (a) enjoy improved power properties, (b) do not suffer from the 'curse of dimensionality' when the vector of covariates is of high-dimensionality, (c) are fully data-driven, (e) do not require tuning parameters such as bandwidths, and (e) are able to detect a broad class of local alternatives converging to the null at the parametric rate. These appealing features highlight that the tests can be of great use in practice.

It is worth stressing that this package implements in a unified manner a large class of specification tests, depending on the chosen weight function w(q,u):

  • ‘ind’ - the indicator weight function w(q,u)=1(q ≤ u). This is the default.

  • ‘exp’ - the exponential weight function w(q,u)=exp(qu).

  • ‘logistic’ - the logistic weight function w(q,u)=1/[1+exp(1-qu)].

  • ‘sin’ - the sine weight function w(q,u)=sin(qu).

  • ‘sincos’ - the sine and cosine weight function w(q,u)=sin(qu)+cos(qu).

Different weight functions w(q,u) have different power properties, and therefore, being able to choose different w(q,u) gives us flexibility to direct power in desired directions.

References

Sant'Anna, Pedro H. C, and Song, Xiaojun (2016), Specification Tests for the Propensity Score, available at http://sites.google.com/site/pedrohcsantanna/.


pedrohcgs/pstest documentation built on July 24, 2022, 7:39 a.m.