Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics

• URL: http://arxiv.org/abs/2304.01316v1
• Date: Mon, 3 Apr 2023 19:32:30 GMT
• Title: Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics
• Authors: Marco Morucci, Cynthia Rudin, Alexander Volfovsky
• Abstract summary: We introduce Matched Machine Learning, a framework that combines the flexibility of machine learning black boxes with the interpretability of matching. Our framework uses machine learning to learn an optimal metric for matching units and estimating outcomes. We show empirically that instances of Matched Machine Learning perform on par with black-box machine learning methods and better than existing matching methods for similar problems.
• Score: 87.05961347040237
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce Matched Machine Learning, a framework that combines the flexibility of machine learning black boxes with the interpretability of matching, a longstanding tool in observational causal inference. Interpretability is paramount in many high-stakes application of causal inference. Current tools for nonparametric estimation of both average and individualized treatment effects are black-boxes that do not allow for human auditing of estimates. Our framework uses machine learning to learn an optimal metric for matching units and estimating outcomes, thus achieving the performance of machine learning black-boxes, while being interpretable. Our general framework encompasses several published works as special cases. We provide asymptotic inference theory for our proposed framework, enabling users to construct approximate confidence intervals around estimates of both individualized and average treatment effects. We show empirically that instances of Matched Machine Learning perform on par with black-box machine learning methods and better than existing matching methods for similar problems. Finally, in our application we show how Matched Machine Learning can be used to perform causal inference even when covariate data are highly complex: we study an image dataset, and produce high quality matches and estimates of treatment effects.

Related papers

• Machine Unlearning for Causal Inference [0.6621714555125157]
  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. The dataset used in the study is the Lalonde dataset, a widely used dataset for evaluating the effectiveness of job training programs.
  arXiv  Detail & Related papers  (2023-08-24T17:27:01Z)
• Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms [88.93372675846123]
  We propose a task-agnostic evaluation framework Camilla for evaluating machine learning algorithms. We use cognitive diagnosis assumptions and neural networks to learn the complex interactions among algorithms, samples and the skills of each sample. In our experiments, Camilla outperforms state-of-the-art baselines on the metric reliability, rank consistency and rank stability.
  arXiv  Detail & Related papers  (2023-07-14T03:15:56Z)
• Variable Importance Matching for Causal Inference [73.25504313552516]
  We describe a general framework called Model-to-Match that achieves these goals. Model-to-Match uses variable importance measurements to construct a distance metric. We operationalize the Model-to-Match framework with LASSO.
  arXiv  Detail & Related papers  (2023-02-23T00:43:03Z)
• Prediction-Powered Inference [68.97619568620709]
  Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system. The framework yields simple algorithms for computing provably valid confidence intervals for quantities such as means, quantiles, and linear and logistic regression coefficients. Prediction-powered inference could enable researchers to draw valid and more data-efficient conclusions using machine learning.
  arXiv  Detail & Related papers  (2023-01-23T18:59:28Z)
• Actionable Interpretation of Machine Learning Models for Sequential Data: Dementia-related Agitation Use Case [0.0]
  Actionable interpretation can be implemented in most traditional black-box machine learning models. It uses the already trained model, used training data, and data processing techniques to extract actionable items. It is shown that actionable items can be extracted, such as the decreasing of in-home light level, which is triggering an agitation episode.
  arXiv  Detail & Related papers  (2020-09-10T19:04:12Z)
• Modeling Generalization in Machine Learning: A Methodological and Computational Study [0.8057006406834467]
  We use the concept of the convex hull of the training data in assessing machine learning generalization. We observe unexpectedly weak associations between the generalization ability of machine learning models and all metrics related to dimensionality.
  arXiv  Detail & Related papers  (2020-06-28T19:06:16Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)
• Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India [3.3449509626538543]
  We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. Key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units.
  arXiv  Detail & Related papers  (2017-12-13T14:47:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.