Causal Identification

13 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

Adapting Text Embeddings for Causal Inference

blei-lab/causal-text-embeddings 29 May 2019

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

causalailab/neuralcausalmodels 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.

Identifying Causal Structure in Dynamical Systems

baumanndominik/identifying_causal_structure 6 Jun 2020

In this paper, we propose a method that identifies the causal structure of control systems.

Invariant Representation Learning for Treatment Effect Estimation

claudiashi57/nice 24 Nov 2020

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

JiajingZ/CopSens 18 Feb 2021

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

shinkaiika/noise-level-causal-identification-additive-noise-models 24 Aug 2021

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.

Causal Discovery via Conditional Independence Testing with Proxy Variables

lmz123321/proxy_causal_discovery 9 May 2023

Distinguishing causal connections from correlations is important in many scenarios.

Causal Discovery from Subsampled Time Series with Proxy Variables

lmz123321/proxy_causal_discovery NeurIPS 2023

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

phlippe/biscuit 16 Jun 2023

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.