Causal Identification
12 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Causal Identification
BCOPA-CE
Latest papers
Hierarchical Causal Models
Scientists often want to learn about cause and effect from hierarchical data, collected from subunits nested inside units.
RCT Rejection Sampling for Causal Estimation Evaluation
We contribute a new sampling algorithm, which we call RCT rejection sampling, and provide theoretical guarantees that causal identification holds in the observational data to allow for valid comparisons to the ground-truth RCT.
BISCUIT: Causal Representation Learning from Binary Interactions
phlippe/biscuit
•
•
Identifying the causal variables of an environment and how to intervene on them is of core value in applications such as robotics and embodied AI.
Causal Discovery from Subsampled Time Series with Proxy Variables
Based on these, we can leverage the proxies to remove the bias induced by the hidden variables and hence achieve identifiability.
Causal Discovery with Unobserved Variables: A Proxy Variable Approach
Our observation is that discretizing continuous variables can can lead to serious errors and comprise the power of the proxy.
Flow-based Perturbation for Cause-effect Inference
ShaogangRen/EFRE
•
•
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM) 2022
A new causal discovery method is introduced to solve the bivariate causal discovery problem.
The Effect of Noise Level on Causal Identification with Additive Noise Models
Unfortunately, one aspect of these methods has not received much attention until now: what is the impact of different noise levels on the ability of these methods to identify the direction of the causal relationship.
The Causal-Neural Connection: Expressiveness, Learnability, and Inference
zecevic-matej/tractable-neural-causal-model
•
•
NeurIPS 2021
Given this property, one may be tempted to surmise that a collection of neural nets is capable of learning any SCM by training on data generated by that SCM.
Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding
Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments.
Invariant Representation Learning for Treatment Effect Estimation
To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding.
