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Found 11 papers, 4 papers with code
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates.
Interpretable Almost Matching Exactly for Causal Inference
Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space.
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference
In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets.
A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence
The study of causality or causal inference - how much a given treatment causally affects a given outcome in a population - goes way beyond correlation or association analysis of variables, and is critical in making sound data driven decisions and policies in a multitude of applications.
Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases
The best current methods for exactly computing the number of satisfying assignments, or the satisfying probability, of Boolean formulas can be seen, either directly or indirectly, as building 'decision-DNNF' (decision decomposable negation normal form) representations of the input Boolean formulas.
Interpretable Almost-Matching-Exactly With Instrumental Variables
Uncertainty in the estimation of the causal effect in observational studies is often due to unmeasured confounding, i. e., the presence of unobserved covariates linking treatments and outcomes.
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others' outcomes.
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.
A Double Machine Learning Approach to Combining Experimental and Observational Data
Experimental and observational studies often lack validity due to untestable assumptions.
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.
