Causal Identification
12 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Causal Identification
Most implemented papers
Adapting Text Embeddings for Causal Inference
To address this challenge, we develop causally sufficient embeddings, low-dimensional document representations that preserve sufficient information for causal identification and allow for efficient estimation of causal effects.
The Causal-Neural Connection: Expressiveness, Learnability, and Inference
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.
Identifying Causal Structure in Dynamical Systems
In this paper, we propose a method that identifies the causal structure of control systems.
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.
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.
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.
Flow-based Perturbation for Cause-effect Inference
A new causal discovery method is introduced to solve the bivariate causal discovery problem.
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.
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.
BISCUIT: Causal Representation Learning from Binary Interactions
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.