# Characterization of parameters with a mixed bias property

## TL;DR

This paper studies a class of parameters with a 'mixed bias property' that allows for robust estimation in non-parametric models, including causal inference parameters, by characterizing their influence functions and deriving functional equations.

## Contribution

It introduces and characterizes the class of parameters with the mixed bias property, expanding on existing classes and providing tools for robust estimation.

## Key findings

- The mixed bias property class includes many parameters of interest in causal inference.
- Derived influence functions and functional equations for parameters with this property.
- Proposed loss functions enable penalized estimation of nuisance functions.

## Abstract

In this article we study a class of parameters with the so-called `mixed bias property'. For parameters with this property, the bias of the semiparametric efficient one step estimator is equal to the mean of the product of the estimation errors of two nuisance functions. In non-parametric models, parameters with the mixed bias property admit so-called rate doubly robust estimators, i.e. estimators that are consistent and asymptotically normal when one succeeds in estimating both nuisance functions at sufficiently fast rates, with the possibility of trading off slower rates of convergence for the estimator of one of the nuisance functions with faster rates for the estimator of the other nuisance. We show that the class of parameters with the mixed bias property strictly includes two recently studied classes of parameters which, in turn, include many parameters of interest in causal inference. We characterize the form of parameters with the mixed bias property and of their influence functions. Furthermore, we derive two functional moment equations, each being solved at one of the two nuisance functions, as well as, two functional loss functions, each being minimized at one of the two nuisance functions. These loss functions can be used to derive loss based penalized estimators of the nuisance functions.