# Models for the Propensity Score that Contemplate the Positivity Assumption and their Application to Missing Data and Causality

## TL;DR

This paper proposes models for propensity scores that explicitly incorporate a lower bound to address the positivity assumption, improving the stability of inverse probability weighting estimators in cases with continuous covariates.

## Contribution

It introduces a new modeling approach for propensity scores that ensures a lower bound, reconciling the positivity assumption with generalized linear models.

## Key findings

- Provides a model with explicit lower bounds for propensity scores.
- Enhances the stability of IPW estimators when positivity does not hold.
- Addresses issues with continuous covariates in propensity score modeling.

## Abstract

Generalized linear models are often assumed to fit propensity scores, which are used to compute inverse probability weighted (IPW) estimators. In order to derive the asymptotic properties of IPW estimators, the propensity score is supposed to be bounded away from cero. This condition is known in the literature as strict positivity (or positivity assumption) and, in practice, when it does not hold, IPW estimators are very unstable and have a large variability. Although strict positivity is often assumed, it is not upheld when some of the covariates are continuous. In this work, we attempt to conciliate between the strict positivity condition and the theory of generalized linear models by incorporating an extra parameter, which results in an explicit lower bound for the propensity scores.