Machine Unlearning for Causal Inference
Vikas Ramachandra Mohit Sethi
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.