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 implements the
data-driven nonparametric diagnostic tools for detecting propensity score
misspecification proposed by Sant'Anna and Song (2019)

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.

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(-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. Thus, being able to choose between different w(q,u) gives one the flexibility to direct power in alternative directions.

For further details, please see the paper 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/.

This github website hosts the source code. To install the `pstest`

package, you simply need to run the following two lines of code in R:

```
library(devtools)
install_github("pedrohcgs/pstest")
```

Pedro H. C. Sant'Anna, Vanderbilt University, Nashville, TN. E-mail: pedro.h.santanna [at] vanderbilt [dot] edu.

Xiaojun Song, Peking University, Beijing, China. E-mail: sxj [at] gsm [dot] pku [dot] edu [dot] cn.

In case you have questions, please do not hesitate to contact us.