Machine learning models play a vital role in making predictions and deriving
insights from data and are being increasingly used for causal inference. To
preserve user privacy, it is important to enable the model to forget some of
its learning/captured information about a given user (machine unlearning). This
paper introduces the concept of machine unlearning for causal inference,
particularly propensity score matching and treatment effect estimation, which
aims to refine and improve the performance of machine learning models for
causal analysis given the above unlearning requirements. The paper presents a
methodology for machine unlearning using a neural network-based propensity
score model. The dataset used in the study is the Lalonde dataset, a widely
used dataset for evaluating the effectiveness i.e. the treatment effect of job
training programs. The methodology involves training an initial propensity
score model on the original dataset and then creating forget sets by
selectively removing instances, as well as matched instance pairs. based on
propensity score matching. These forget sets are used to evaluate the retrained
model, allowing for the elimination of unwanted associations. The actual
retraining of the model is performed using the retain set. The experimental
results demonstrate the effectiveness of the machine unlearning approach. The
distribution and histogram analysis of propensity scores before and after
unlearning provide insights into the impact of the unlearning process on the
data. This study represents the first attempt to apply machine unlearning
techniques to causal inference.

该研究将机器遗忘技术应用于因果推断，提出了一种基于神经网络的倾向分数模型的机器遗忘方法，通过选择性地删除实例和匹配的实例对，评估重新训练的模型并消除不需要的关联，实验结果表明机器遗忘方法的有效性，并通过倾向分数的分布和直方图分析展示无关联数据对数据的影响。

机器反学习的因果推断

Machine Unlearning for Causal Inference

In this paper, we propose deep learning techniques for econometrics,
specifically for causal inference and for estimating individual as well as
average treatment effects. The contribution of this paper is twofold: 1. For
generalized neighbor matching to estimate individual and average treatment
effects, we analyze the use of autoencoders for dimensionality reduction while
maintaining the local neighborhood structure among the data points in the
embedding space. This deep learning based technique is shown to perform better
than simple k nearest neighbor matching for estimating treatment effects,
especially when the data points have several features/covariates but reside in
a low dimensional manifold in high dimensional space. We also observe better
performance than manifold learning methods for neighbor matching. 2. Propensity
score matching is one specific and popular way to perform matching in order to
estimate average and individual treatment effects. We propose the use of deep
neural networks (DNNs) for propensity score matching, and present a network
called PropensityNet for this. This is a generalization of the logistic
regression technique traditionally used to estimate propensity scores and we
show empirically that DNNs perform better than logistic regression at
propensity score matching. Code for both methods will be made available shortly
on Github at: this https URL

本研究提出一种基于深度学习的经济计量学技术，特别是针对因果推断和估计个体和平均处理效果。具体而言，我们分析了使用自编码器进行降维的广义邻居匹配用于估计个体处理效果和平均处理效果，并将深度神经网络应用于倾向得分匹配以提高性能。