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Machine Unlearning for Causal Inference

Vikas Ramachandra, Mohit Sethi

arXiv:2308.13559v1cs.LG

TL;DR

Machine unlearning had not previously been applied to causal inference, where removing biases and unwanted associations is a stated goal. The paper retrains propensity-score models after propensity-matched removals and reports better unlearning than random removal.

  • Problem

    Machine unlearning had not previously been applied to causal inference, where eliminating biases and unwanted associations is presented as a goal for more reliable inferences.

    Source: Introduction · pp. 2, 4

  • Method

    The study trains an initial propensity-score model, removes propensity-matched instance pairs to form forget sets, and retrains on retain sets.

    Source: Introduction · p. 3

  • Results

    Pair-wise propensity-matching removal was reported as more effective for unlearning than random removal, with reduced propensity-score overlap and lower RMSE values on the original dataset.

    Source: Results · p. 7, Conclusion · p. 9

  • Takeaways & Limitations

    The study frames machine unlearning as a means for researchers to evaluate the quality of causal analyses.

    Source: Introduction · p. 4

Abstract

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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.

Introduction

This paper introduces machine unlearning for causal inference to remove unwanted associations, using propensity-score modeling and the Lalonde dataset to demonstrate the approach.

  • Experiments use Lalonde, training an initial propensity-score model, removing matched instance pairs into forget sets, and retraining on retain sets [4].The dataset evaluates job-training effectiveness; the method compares retrained and original models using RMSE.Source: Introduction · p. 3
  • The analysis compares propensity-score distributions and histograms before and after unlearning to examine changes in data associations and biases.Source: Introduction · p. 3
  • The paper argues that unlearning can address privacy concerns while retaining causal information, supporting more reliable and fair decisions on sensitive data.Source: Introduction · p. 3
  • The study presents the first attempt to apply machine unlearning to causal inference, targeting biases and unwanted associations to improve causal-inference reliability [2].It frames unlearning as refining models while retaining causal information.Source: Introduction · pp. 2, 4

Datasets & Methods:

The study applies machine unlearning to propensity-score models for causal inference using the Lalonde job-training dataset, forming forget sets, retraining on retained instances, and examining propensity-score changes.

Datasets & Methods:

  • The study uses the Lalonde job-training benchmark, with covariates such as age, education, race, and income, treatment participation, and post-program income as the outcome.Numerical covariates are standardized using scikit-learn’s StandardScaler.Source: Datasets & Methods · p. 4
  • A neural network estimates propensity scores from covariates, using ReLU linear layers, a sigmoid output, binary cross-entropy, and Adam; the initial model is trained on the full dataset.The propensity-score modeling approach is borrowed from “Deep Learning for Causal Inference” [8].Source: Datasets & Methods · p. 4
  • Forget sets comprise propensity-matched treatment–control pairs or randomly selected instances from both groups; models are retrained on the remaining retain set.The pair-wise approach uses nearest neighbors to select a control instance for each treatment instance, and retraining uses the original model architecture and training procedure.Source: Datasets & Methods · pp. 4–5
  • Retrained Models 2 and 3 are assessed by RMSE for treatment-effect estimation against the original model trained on the full dataset.The original model serves as the benchmark.Source: Datasets & Methods · p. 5
  • Kernel density estimation and histograms compare propensity-score distributions before and after retraining to inspect shifts in associations and biases.The study combines forget-set creation, retraining, and propensity-score analysis to explore machine unlearning for refining propensity-score models and causal inference [2].Source: Datasets & Methods · p. 5

Results:

On the original dataset, pair-wise propensity-matching removal produced less overlap in propensity-score distributions but a higher RMSE than the full-data model; random removal produced near-complete overlap and a similar RMSE.

Results

  • Pair-wise-removal forget set 1 showed less propensity-score distribution overlap than the original dataset, whereas random-removal forget set 2 showed almost complete overlap.The paper associates pair-wise removal with reduced biases and associations, while random removal did not significantly alter them.Source: Results · p. 6
  • On the original dataset, RMSE was 0.0579 for Model 1, 0.1087 for Model 2, and 0.0592 for Model 3; the authors interpret Model 2 as better unlearning.Model 1 used the entire dataset; Models 2 and 3 were retrained on retain sets created by pair-wise propensity-matching removal and random instance removal, respectively.Source: Results · pp. 6–7
  • The authors conclude propensity-score-matching pair-wise removal is more effective for machine unlearning in causal analysis and may refine propensity-score models for causal inference [2].They cite reduced propensity-score distribution overlap as evidence that the approach reduces biases and associations.Source: Results · p. 7

Conclusion:

The study applies machine unlearning to causal inference using propensity-score models and finds pair-wise removal more effective than random removal. It highlights the promise of unlearning for causal analysis and calls for testing other techniques, datasets, and tasks.

Conclusion:

  • Pair-wise removal with propensity score matching achieved better unlearning than random removal, with lower original-dataset RMSE and less overlap between forget set 1 and original propensity-score distributions.The reduced overlap was interpreted as evidence that the model had successfully unlearned.Source: Conclusion · p. 9
  • Random removal [4] produced minimal changes in model associations and biases, with near-complete propensity-score distribution overlap for forget set 2 and comparable original-dataset RMSE.These results suggest limited unlearning.Source: Conclusion · p. 9
  • The findings emphasize careful selection of unlearning approach, with propensity-matching pair-wise removal promising for reducing model biases and associations and improving causal inference [2].The study evaluates machine unlearning techniques on a causal dataset using propensity-score models.Source: Conclusion · pp. 8–9
  • The study contributes to research on machine unlearning in causal analysis and calls for testing other techniques, datasets, and causal inference tasks [2].It identifies potential for refining models and enhancing the robustness and reliability of causal studies.Source: Conclusion · p. 9
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