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.

In short, this package implements the class of specification test for the propensity score proposed in Sant’Anna and Song (2019), Specification Tests for the Propensity Score. The package accommodates Logit and Probit propensity score specifications. 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 the `pstest`

package implements in a unified manner a large class of specification tests, depending on the chosen weight function w(q,u). Current choices are:

`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. 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 (2019), Specification Tests for the Propensity Score, or the working paper version.

This github website hosts the source code. The difference between what is here what is in CRAN is that here we always have the most updated version of the package.

To install the `pstest`

package, you have two options: (a) install the CRAN version, or (b) instal the GitHub (most updated - RECOMMENDED) version.

You can install the package from CRAN with `install.packages("pstest")`

.

Alternatively, you can install the most recent version of the `pstest`

package from GitHub (this is what we recommend):

```
library(devtools)
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.