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Found 12 papers, 3 papers with code
Causal Relational Learning
Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making.
MALTS: Matching After Learning to Stretch
In this work, we learn an interpretable distance metric for matching, which leads to substantially higher quality matches.
Validating Causal Inference Methods
Thus simulated data sets are used to evaluate the potential performance of various causal estimation methods when applied to data similar to the observed sample.
Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients
Having a maximum EA burden greater than 75% when untreated had a 22% increased chance of a poor outcome (severe disability or death), and mild but long-lasting EA increased the risk of a poor outcome by 14%.
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.
Variable Importance Matching for Causal Inference
Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, accurate for treatment effect estimation, and scalable to high-dimensional data.
A Double Machine Learning Approach to Combining Experimental and Observational Data
Experimental and observational studies often lack validity due to untestable assumptions.
Safe and Interpretable Estimation of Optimal Treatment Regimes
Our work operationalizes a safe and interpretable framework to identify optimal treatment regimes.
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors.
Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population
Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation.
Towards Generalizing Inferences from Trials to Target Populations
Randomized Controlled Trials (RCTs) are pivotal in generating internally valid estimates with minimal assumptions, serving as a cornerstone for researchers dedicated to advancing causal inference methods.
Graph Neural Network based Double Machine Learning Estimator of Network Causal Effects
Our paper addresses the challenge of inferring causal effects in social network data, characterized by complex interdependencies among individuals resulting in challenges such as non-independence of units, interference (where a unit's outcome is affected by neighbors' treatments), and introduction of additional confounding factors from neighboring units.
