Papers Causal Identification
“Causal Identification” 태그가 달린 논문 48편 · 필터 해제
Causal Identification in Time Series Models
In this paper, we analyze the applicability of the Causal Identification algorithm to causal time series graphs with latent confounders. Since these graphs extend over infinitely many time steps, deciding whether causal …
Causal IdentificationTime SeriesGenerative AI in Live Operations: Evidence of Productivity Gains in Cybersecurity and Endpoint Management
We measure the association between generative AI (GAI) tool adoption and four metrics spanning security operations, information protection, and endpoint management: 1) number of security alerts per incident, 2) probabili…
Causal IdentificationManagementGenerative AI and Security Operations Center Productivity: Evidence from Live Operations
We measure the association between generative AI (GAI) tool adoption and security operations center productivity. We find that GAI adoption is associated with a 30.13% reduction in security incident mean time to resoluti…
Causal IdentificationJob Loss and Political Entry
The supply of politicians affects the quality of democratic institutions. Yet little is known about the economic motivations that drive individuals into politics. This paper examines how experiencing a job loss affects i…
Causal IdentificationNew Rules for Causal Identification with Background Knowledge
Identifying causal relations is crucial for a variety of downstream tasks. In additional to observational data, background knowledge (BK), which could be attained from human expertise or experiments, is usually introduce…
Causal IdentificationIdentifying while Learning for Document Event Causality Identification
Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document. Existing studies adopt a kind of identifying after learning paradigm, where events' representat…
Causal IdentificationEvent Causality IdentificationCausal Inference from Slowly Varying Nonstationary Processes
Causal inference from observational data following the restricted structural causal models (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussia…
Causal IdentificationCausal InferenceTime SeriesAlgorithmic syntactic causal identification
Causal identification in causal Bayes nets (CBNs) is an important tool in causal inference allowing the derivation of interventional distributions from observational distributions where this is possible in principle. How…
Causal IdentificationCausal InferenceCause and Effect: Can Large Language Models Truly Understand Causality?
With the rise of Large Language Models(LLMs), it has become crucial to understand their capabilities and limitations in deciphering and explaining the complex web of causal relationships that language entails. Current me…
Causal DiscoveryCausal IdentificationcounterfactualCounterfactual ReasoningBridging Methodologies: Angrist and Imbens' Contributions to Causal Identification
In the 1990s, Joshua Angrist and Guido Imbens studied the causal interpretation of Instrumental Variable estimates (a widespread methodology in economics) through the lens of potential outcomes (a classical framework to …
ArticlesCausal IdentificationHierarchical Causal Models
Scientists often want to learn about cause and effect from hierarchical data, collected from subunits nested inside units. Consider students in schools, cells in patients, or cities in states. In such settings, unit-leve…
Causal IdentificationTowards Bounding Causal Effects under Markov Equivalence
Predicting the effect of unseen interventions is a fundamental research question across the data sciences. It is well established that in general such questions cannot be answered definitively from observational data. Th…
Causal IdentificationThe Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation
Assessing causal effects in the presence of unobserved confounding is a challenging problem. Existing studies leveraged proxy variables or multiple treatments to adjust for the confounding bias. In particular, the latter…
Causal DiscoveryCausal IdentificationNeural Network Parameter-optimization of Gaussian pmDAGs
Finding the parameters of a latent variable causal model is central to causal inference and causal identification. In this article, we show that existing graphical structures that are used in causal inference are not sta…
Causal IdentificationCausal InferenceThe role of causality in explainable artificial intelligence
Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation share common ancient roots. This is further …
Causal DiscoveryCausal IdentificationCausal InferenceExplainable artificial intelligence+4Optimal and Fair Encouragement Policy Evaluation and Learning
In consequential domains, it is often impossible to compel individuals to take treatment, so that optimal policy rules are merely suggestions in the presence of human non-adherence to treatment recommendations. Under het…
Causal IdentificationFairnessActive and Passive Causal Inference Learning
This paper serves as a starting point for machine learning researchers, engineers and students who are interested in but not yet familiar with causal inference. We start by laying out an important set of assumptions that…
Causal IdentificationCausal InferenceRCT Rejection Sampling for Causal Estimation Evaluation
Confounding is a significant obstacle to unbiased estimation of causal effects from observational data. For settings with high-dimensional covariates -- such as text data, genomics, or the behavioral social sciences -- r…
Causal IdentificationBISCUIT: 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. While an agent can commonly interact with the environment and may implici…
Causal DiscoveryCausal IdentificationRepresentation LearningCausal Discovery via Conditional Independence Testing with Proxy Variables
Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing com…
Causal DiscoveryCausal Identification