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Found 15 papers, 5 papers with code
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome
In this paper, we present a novel approach that tracks a user's latent personality dimensions (LPDs) during ongoing persuasion conversation and generates tailored counterfactual utterances based on these LPDs to optimize the overall persuasion outcome.
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
This particular test-wise deletion procedure, in which we perform CI tests on the samples without zeros for the conditioned variables, can be seamlessly integrated with existing structure learning approaches including constraint-based and greedy score-based methods, thus giving rise to a principled framework for GRNI in the presence of dropouts.
Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework
Large language models (LLMs) can easily generate biased and discriminative responses.
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems.
Procedural Fairness Through Decoupling Objectionable Data Generating Components
We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i. e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals.
Causal-learn: Causal Discovery in Python
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering.
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models
Causal discovery under measurement error aims to recover the causal graph among unobserved target variables from observations made with measurement error.
The m-connecting imset and factorization for ADMG models
The m-connecting imset and factorization criterion provide two new statistical tools for learning and inference with ADMG models.
Causal discovery for observational sciences using supervised machine learning
We use random subsampling to investigate real data performance on small samples and again find that SLdisco is less sensitive towards sample size and hence seems to better utilize the information available in small datasets.
A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption
Kalisch and B\"{u}hlmann (2007) showed that for linear Gaussian models, under the Causal Markov Assumption, the Strong Causal Faithfulness Assumption, and the assumption of causal sufficiency, the PC algorithm is a uniformly consistent estimator of the Markov Equivalence Class of the true causal DAG for linear Gaussian models; it follows from this that for the identifiable causal effects in the Markov Equivalence Class, there are uniformly consistent estimators of causal effects as well.
Learning from Positive and Unlabeled Data by Identifying the Annotation Process
Yet the annotation process is an important part of the data collection, and in many cases it naturally depends on certain features of the data (e. g., the intensity of an image and the size of the object to be detected in the image).
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions
This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of measurement error with unknown variance.
Mixed Graphical Models for Causal Analysis of Multi-modal Variables
Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data.
Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (2010)
This is the Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, which was held on Catalina Island, CA, July 8 - 11 2010.
Nonlinear directed acyclic structure learning with weakly additive noise models
This results in a more computationally efficient approach that is useful for arbitrary distributions even when additive noise models are invertible.
