Causal Inference
430 papers with code • 3 benchmarks • 8 datasets
Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.
( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )
Libraries
Use these libraries to find Causal Inference models and implementations
IHDP
NTPairs
ICLR Database
Most implemented papers
Subset verification and search algorithms for causal DAGs
In this work, we study the problem of identifying the smallest set of interventions required to learn the causal relationships between a subset of edges (target edges).
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets
vanderschaarlab/mlforhealthlabpub
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ICLR 2018
Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual's potential outcomes to be learned from biased data and without having access to the counterfactuals.
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance.
Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
Causal inference analysis is the estimation of the effects of actions on outcomes.
The Blessings of Multiple Causes
Causal inference from observational data often assumes "ignorability," that all confounders are observed.
Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs
In non-experimental settings, the Regression Discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference.
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
ioanabica/Time-Series-Deconfounder
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ICML 2020
The estimation of treatment effects is a pervasive problem in medicine.
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
octeufer/Adversarial-Balancing-based-representation-learning-for-Causal-Effect-Inference
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The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, where there exists confounding bias; on the other hand, we have to deal with the identification of CATE when the distribution of covariates in treatment and control groups are imbalanced.
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference
Thus, the toolkit is agnostic to the machine learning model that is used.
Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
usaito/unbiased-implicit-rec-real
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Subsequently, we analyze the variance of the proposed unbiased estimator and further propose a clipped estimator that includes the unbiased estimator as a special case.
