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

Parikh, Harsh; Rudin, Cynthia; Volfovsky, Alexander

doi:10.1353/obs.2019.0006

Cited by 8 publications

(14 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here treatment units are matched to similar control units to estimate treatment effects. We refer readers to recent work on this topic for more details Parikh et al, 2019).…”

Section: Modern Case-based Reasoningmentioning

confidence: 99%

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Rudin¹,

Chen²,

Chen³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical challenge areas in interpretable machine learning and provide history and background on each problem. Some of these problems are classically important, and some are recent problems that have arisen in the last few years. These problems are: (1) Optimizing sparse logical models such as decision trees; (2) Optimization of scoring systems; (3) Placing constraints into generalized additive models to encourage sparsity and better interpretability; (4) Modern case-based reasoning, including neural networks and matching for causal inference; (5) Complete supervised disentanglement of neural networks; (6) Complete or even partial unsupervised disentanglement of neural networks; (7) Dimensionality reduction for data visualization; (8) Machine learning models that can incorporate physics and other generative or causal constraints; (9) Characterization of the "Rashomon set" of good models; and (10) Interpretable reinforcement learning. This survey is suitable as a starting point for statisticians and computer scientists interested in working in interpretable machine learning. 1

show abstract

“…Here treatment units are matched to similar control units to estimate treatment effects. We refer readers to recent work on this topic for more details Parikh et al, 2019).…”

Section: Modern Case-based Reasoningmentioning

confidence: 99%

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Rudin¹,

Chen²,

Chen³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…These projections can be sensitive to modeling choices that affect the accuracy of the treatment effect estimates [Kreif et al, 2016]. Further, the units within a matched group can be far from each other in covariate space -i.e., the matched groups are generally not auditable [Parikh et al, 2022b]. To date, the only observational causal inference techniques that attempt to optimize accuracy while maintaining auditability are those stemming from the almost-matchingexactly (AME) framework, namely optimal matching (optMatch) [Yu et al, 2021, Kallus, 2017, genetic matching (GenMatch) [Diamond and Sekhon, 2013], FLAME/DAME [Wang et al, 2017, Dieng et al, 2019, MALTS [Parikh et al, 2022b[Parikh et al, , 2019[Parikh et al, , 2022a and AHB [Morucci et al, 2020] algorithms.…”

Section: Background and Assumptionsmentioning

confidence: 99%

“…Existing almost-matching-exactly methods learn covariate weights and/or create match groups through computationally expensive and data hungry optimization algorithms. In this section we compare LCM to MALTS [ Parikh et al, 2022b], GenMatch [Diamond and Sekhon, 2013], and AHB [Morucci et al, 2020] in regards to scalability in runtime and in CATE estimation accuracy. We omit FLAME/DAME [Wang et al, 2017, Dieng et al, 2019 from this comparison since it can only handle discrete covariates.…”

Section: Scalabilitymentioning

confidence: 99%

“…We would ideally be able to match units that are exactly identical to one another except for treatment assignments [Rosenbaum and Rubin, 1983]. However, exact matches are almost impossible in high-dimensional settings with continuous covariates [Parikh et al, 2022b]. In such scenarios, we aim to create almost-exact matches on important covariates.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference

Quinn¹,

Parikh²,

Volfovsky³

et al. 2023

Preprint

View full text Add to dashboard Cite

Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, yield accurate treatment effect estimates, and scalable to high-dimensional data. We describe an almost-exact matching approach that achieves these goals by (i) learning a distance metric via outcome modeling, (ii) creating matched groups using the distance metric, and (iii) using the matched groups to estimate treatment effects. Our proposed method uses variable importance measurements to construct a distance metric, making it a flexible method that can be adapted to various applications. Concentrating on the scalability of the problem in the number of potential confounders, we operationalize our approach with LASSO. We derive performance guarantees for settings where LASSO outcome modeling consistently identifies all confounders (importantly without requiring the linear model to be correctly specified). We also provide experimental results demonstrating the auditability of matches, as well as extensions to more general nonparametric outcome modeling.

show abstract

“…These matched groups are used to estimate heterogeneous causal effects with high accuracy. Previous work on MALTS shows that it performs on-par with contemporary black-box causal machine learning methods while also ensuring interpretability (Parikh et al, 2020(Parikh et al, , 2019.…”

Section: Interpretable-and-accurate Causal Inferencementioning

confidence: 99%

Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It

Parikh¹,

Hoffman²,

Sun³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Many fundamental problems affecting the care of critically ill patients lead to similar analytical challenges: physicians cannot easily estimate the effects of at-risk medical conditions or treatments (which is problematic for treatment decisions) because the causal effects of medical conditions and drugs are entangled. They also cannot easily perform studies: there are not enough critically ill patients for high-dimensional observational causal inference analysis, and randomized controlled trials often cannot ethically be conducted. However, mechanistic knowledge is available, including how drugs are absorbed into the body, and the combination of this knowledge with the limited data could potentially suffice -if we knew how to combine them. In this work, we present a framework for interpretable estimation of causal effects for critically ill patients under exactly these complex conditions: interactions between drugs and observations over time, patient data sets that are not large, and mechanistic knowledge that can substitute for lack of data. Our framework incorporates pharmacokinetics and pharmacodynamics with interpretable matching methods to adjust for confounders such as patients' drug response, medical history, and demographic variables. We apply this framework to an extremely important problem affecting critically ill patients, namely the effect of seizures and other potentially harmful electrical events in the brain (called epileptiform activity -EA) on outcomes. EA is a key indicator of whether the patient will suffer long term severe

show abstract

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

Cited by 8 publications

References 1 publication

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference

Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It

Contact Info

Product

Resources

About