Normalized Augmented Inverse Probability Weighting with Neural Network Predictions

08/03/2021
by   Mehdi Rostami, et al.
0

The estimation of Average Treatment Effect (ATE) as a causal parameter is carried out in two steps, where in the first step, the treatment and outcome are modeled to incorporate the potential confounders, and in the second step, the predictions are inserted into the ATE estimators such as the Augmented Inverse Probability Weighting (AIPW) estimator. Due to the concerns regarding the nonlinear or unknown relationships between confounders and the treatment and outcome, there has been an interest in applying non-parametric methods such as Machine Learning (ML) algorithms instead. Some literature proposes to use two separate Neural Networks (NNs) where there's no regularization on the network's parameters except the Stochastic Gradient Descent (SGD) in the NN's optimization. Our simulations indicate that the AIPW estimator suffers extensively if no regularization is utilized. We propose the normalization of AIPW (referred to as nAIPW) which can be helpful in some scenarios. nAIPW, provably, has the same properties as AIPW, that is, the double-robustness and orthogonality properties. Further, if the first step algorithms converge fast enough, under regulatory conditions, nAIPW will be asymptotically normal. We also compare the performance of AIPW and nAIPW in terms of the bias and variance when small to moderate L1 regularization is imposed on the NNs.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/02/2021

The Bias-Variance Tradeoff of Doubly Robust Estimator with Targeted L_1 regularized Neural Networks Predictions

The Doubly Robust (DR) estimation of ATE can be carried out in 2 steps, ...
research
07/20/2023

Multiply Robust Estimator Circumvents Hyperparameter Tuning of Neural Network Models in Causal Inference

Estimation of the Average Treatment Effect (ATE) is often carried out in...
research
11/18/2022

All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples

There is a long-standing debate in the statistical, epidemiological and ...
research
06/05/2019

Adapting Neural Networks for the Estimation of Treatment Effects

This paper addresses the use of neural networks for the estimation of tr...
research
06/14/2021

Adaptive normalization for IPW estimation

Inverse probability weighting (IPW) is a general tool in survey sampling...
research
12/21/2018

Stochastic Doubly Robust Gradient

When training a machine learning model with observational data, it is of...
research
01/27/2023

Convolutional neural networks for valid and efficient causal inference

Convolutional neural networks (CNN) have been successful in machine lear...

Please sign up or login with your details

Forgot password? Click here to reset