An Application of Matching After Learning To Stretch (MALTS) to the ACIC 2018 Causal Inference Challenge Data

By Harsh Parikh, Cynthia Rudin, Alexander Volfovsky. 2021

Abstract:In the learning-to-match framework for causal inference, a parameterized distance metric is trained on a holdout train set so that the matching yields accurate estimated conditional average treatment effects. This way, the matching can be as accurate as other black box machine learning techniques for causal inference. We use a new learning-to-match algorithm called Matching-After-Learning-To-Stretch (MALTS) (Parikh et al., 2018) to study an observational dataset from the Atlantic Causal Inference Challenge. Other than providing estimates for (conditional) average treatment effects, the MALTS procedure allows practitioners to evaluate matched groups directly, understand where more data might need to be collected and gain an understanding of when estimates can be trusted.1

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, neutral): “We refer readers to recent work on this topic for more details (Wang et al., 2021; Parikh et al., 2019).”
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, methodology): “This idea is the foundation for the MALTS algorithm (Parikh et al., 2018; Parikh et al., 2019).”
MALTS: Matching After Learning to Stretch (2018, methodology): “MALTS was previously used on the ACIC 2018 Causal Inference Challenge Data (see Parikh et al. 2019).”