Papers Causal Identification
“Causal Identification” 태그가 달린 논문 48편 · 필터 해제
Causal Discovery from Subsampled Time Series with Proxy Variables
Inferring causal structures from time series data is the central interest of many scientific inquiries. A major barrier to such inference is the problem of subsampling, i.e., the frequency of measurement is much lower th…
Causal DiscoveryCausal IdentificationTime Seriespgmpy: A Python Toolkit for Bayesian Networks
Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It imple…
Causal DiscoveryCausal IdentificationCausal InferenceDecision Making+1Representation Disentaglement via Regularization by Causal Identification
In this work, we propose the use of a causal collider structured model to describe the underlying data generative process assumptions in disentangled representation learning. This extends the conventional i.i.d. factoriz…
Causal IdentificationCausal InferenceDisentanglementRepresentation Learning+1Emerging Synergies in Causality and Deep Generative Models: A Survey
In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance. Deep generative models (DGMs) have proven adept in capturing complex data distr…
Causal IdentificationFairnessNavigateSurveyIdentifying causal effects with subjective ordinal outcomes
Survey questions often ask respondents to select from ordered scales where the meanings of the categories are subjective, leaving each individual free to apply their own definitions in answering. This paper studies the u…
Causal IdentificationCausal InferenceregressionUnityFlow-based Perturbation for Cause-effect Inference
A new causal discovery method is introduced to solve the bivariate causal discovery problem. The proposed algorithm leverages the expressive power of flow-based models and tries to learn the complex relationship between …
Causal DiscoveryCausal IdentificationCausal and Counterfactual Views of Missing Data Models
It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficult because for every experimental unit o…
Causal IdentificationCausal InferencecounterfactualCausal Estimation for Text Data with (Apparent) Overlap Violations
Consider the problem of estimating the causal effect of some attribute of a text document; for example: what effect does writing a polite vs. rude email have on response time? To estimate a causal effect from observation…
AttributeCausal IdentificationRepresentation LearningUncertainty QuantificationCausal Effect Identification in Uncertain Causal Networks
Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these algorithms hinges on the restrictive ass…
Causal IdentificationCausal InferenceCombinatorial OptimizationCausal Identification with Matrix Equations
Causal effect identification is concerned with determining whether a causal effect is computable from a combination of qualitative assumptions about the underlying system (e.g., a causal graph) and distributions collecte…
Causal IdentificationCausal Identification with Additive Noise Models: Quantifying the Effect of Noise
In recent years, a lot of research has been conducted within the area of causal inference and causal learning. Many methods have been developed to identify the cause-effect pairs in models and have been successfully appl…
Causal DiscoveryCausal IdentificationCausal InferenceA Spectral Approach to Off-Policy Evaluation for POMDPs
We consider off-policy evaluation (OPE) in Partially Observable Markov Decision Processes, where the evaluation policy depends only on observable variables but the behavior policy depends on latent states (Tennenholtz et…
Causal IdentificationOff-policy evaluationThe Effect of Noise Level on Causal Identification with Additive Noise Models
In recent years a lot of research has been conducted within the area of causal inference and causal learning. Many methods have been developed to identify the cause-effect pairs in models and have been successfully appli…
Causal DiscoveryCausal IdentificationCausal InferenceSelection biasThe Causal-Neural Connection: Expressiveness, Learnability, and Inference
One of the central elements of any causal inference is an object called structural causal model (SCM), which represents a collection of mechanisms and exogenous sources of random variation of the system under investigati…
Causal IdentificationCausal InferenceInductive BiasEntropic Causal Inference: Identifiability and Finite Sample Results
Entropic causal inference is a framework for inferring the causal direction between two categorical variables from observational data. The central assumption is that the amount of unobserved randomness in the system is n…
Causal IdentificationCausal InferenceCausal Inference from Slowly Varying Nonstationary Processes
Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussian…
Causal DiscoveryCausal IdentificationCausal InferenceTime Series+1Learning Causal Effects via Weighted Empirical Risk Minimization
Learning causal effects from data is a fundamental problem across the sciences. Determining the identifiability of a target effect from a combination of the observational distribution and the causal graph underlying a ph…
Causal IdentificationCausal InferenceInvariant Representation Learning for Treatment Effect Estimation
The defining challenge for causal inference from observational data is the presence of `confounders', covariates that affect both treatment assignment and the outcome. To address this challenge, practitioners collect and…
Causal IdentificationCausal InferenceRepresentation LearningvalidIdentifying Causal Structure in Dynamical Systems
Mathematical models are fundamental building blocks in the design of dynamical control systems. As control systems are becoming increasingly complex and networked, approaches for obtaining such models based on first prin…
Causal IdentificationCausal InferenceIdentification of Conditional Causal Effects under Markov Equivalence
Causal identification is the problem of deciding whether a post-interventional distribution is computable from a combination of qualitative knowledge about the data-generating process, which is encoded in a causal diagra…
Causal Identification