62 research outputs found
MALTS: Matching After Learning to Stretch
We introduce a flexible framework that produces high-quality almost-exact
matches for causal inference. Most prior work in matching uses ad-hoc distance
metrics, often leading to poor quality matches, particularly when there are
irrelevant covariates. In this work, we learn an interpretable distance metric
for matching, which leads to substantially higher quality matches. The learned
distance metric stretches the covariate space according to each covariate's
contribution to outcome prediction: this stretching means that mismatches on
important covariates carry a larger penalty than mismatches on irrelevant
covariates. Our ability to learn flexible distance metrics leads to matches
that are interpretable and useful for the estimation of conditional average
treatment effects.Comment: 40 pages, 5 Tables, 12 Figure
Are Synthetic Control Weights Balancing Score?
In this short note, I outline conditions under which conditioning on
Synthetic Control (SC) weights emulates a randomized control trial where the
treatment status is independent of potential outcomes. Specifically, I
demonstrate that if there exist SC weights such that (i) the treatment effects
are exactly identified and (ii) these weights are uniformly and cumulatively
bounded, then SC weights are balancing scores.Comment: 2 pages, 2 figure
Effective Continuous Student Assessment using Statistical Methods
The need of the hour is to impart knowledge to students especially those who are below average and help them gain foot in competing with confidence and vigor. Engineering courses are no cake walk however; a sense of enthusiasm can be developed in such students to partake later in the conglomeration of experts on completion of their course. For this to happen, proper assessment and evaluation of subjective content during the course must be done. A proper and effective assessment process should facilitate in timely identification of the student�s weak topics in a subject during the course. In this paper, we discuss about direct assessment technique that starts with the preparation of the question paper, pertaining to the subject, topic-wise. The assessment of the student�s answers shall be done and marks of the subject shall be entered topic-wise. When marks obtained for a particular topic of a subject is below a certain threshold, it acts as an alarm to notify the student of their weak topic that requires immediate attention
A Double Machine Learning Approach to Combining Experimental and Observational Data
Experimental and observational studies often lack validity due to untestable
assumptions. We propose a double machine learning approach to combine
experimental and observational studies, allowing practitioners to test for
assumption violations and estimate treatment effects consistently. Our
framework tests for violations of external validity and ignorability under
milder assumptions. When only one assumption is violated, we provide
semi-parametrically efficient treatment effect estimators. However, our
no-free-lunch theorem highlights the necessity of accurately identifying the
violated assumption for consistent treatment effect estimation. We demonstrate
the applicability of our approach in three real-world case studies,
highlighting its relevance for practical settings
Estimating Trustworthy and Safe Optimal Treatment Regimes
Recent statistical and reinforcement learning methods have significantly
advanced patient care strategies. However, these approaches face substantial
challenges in high-stakes contexts, including missing data, inherent
stochasticity, and the critical requirements for interpretability and patient
safety. Our work operationalizes a safe and interpretable framework to identify
optimal treatment regimes. This approach involves matching patients with
similar medical and pharmacological characteristics, allowing us to construct
an optimal policy via interpolation. We perform a comprehensive simulation
study to demonstrate the framework's ability to identify optimal policies even
in complex settings. Ultimately, we operationalize our approach to study
regimes for treating seizures in critically ill patients. Our findings strongly
support personalized treatment strategies based on a patient's medical history
and pharmacological features. Notably, we identify that reducing medication
doses for patients with mild and brief seizure episodes while adopting
aggressive treatment for patients in intensive care unit experiencing intense
seizures leads to more favorable outcomes
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