Causal Inference
413 papers with code • 3 benchmarks • 8 datasets
Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.
( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )
Libraries
Use these libraries to find Causal Inference models and implementationsLatest papers with no code
Semi-Supervised Learning for Deep Causal Generative Models
Training causal generative models that address such counterfactual questions, though, currently requires that all relevant variables have been observed and that corresponding labels are available in training data.
Physics-Based Causal Reasoning for Safe & Robust Next-Best Action Selection in Robot Manipulation Tasks
Safe and efficient object manipulation is a key enabler of many real-world robot applications.
A Transfer Learning Causal Approach to Evaluate Racial/Ethnic and Geographic Variation in Outcomes Following Congenital Heart Surgery
Using the Society of Thoracic Surgeons' Congenital Heart Surgery Database (STS-CHSD), we focus on a national cohort of patients undergoing the Norwood operation from 2016-2022 to assess operative mortality and morbidity outcomes across U. S. geographic regions by race/ethnicity.
Research on Personal Credit Risk Assessment Methods Based on Causal Inference
Due to the limitations in the development of category theory-related technical tools, this paper adopts the widely-used probabilistic causal graph tool proposed by Judea Pearl in 1995 to study the application of causal inference in personal credit risk management.
Lifted Causal Inference in Relational Domains
Lifted inference exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers.
Limits of Approximating the Median Treatment Effect
In the finite population setting containing $n$ individuals, with treatment and control values denoted by the potential outcome vectors $\mathbf{a}, \mathbf{b}$, much of the prior work focused on estimating median$(\mathbf{a}) -$ median$(\mathbf{b})$, where median($\mathbf x$) denotes the median value in the sorted ordering of all the values in vector $\mathbf x$.
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.
Algorithmic syntactic causal identification
Our description is given entirely in terms of the non-parametric ADMG structure specifying a causal model and the algebraic signature of the corresponding monoidal category, to which a sequence of manipulations is then applied so as to arrive at a modified monoidal category in which the desired, purely syntactic interventional causal model, is obtained.
Probabilistic Easy Variational Causal Effect
In this paper, on the one hand, for the case that $X$ and $Z$ are continuous, by using the ideas from the total variation and the flux of $g$, we develop a point of view in causal inference capable of dealing with a broad domain of causal problems.
Imputation of Counterfactual Outcomes when the Errors are Predictable
Often overlooked is the possibility that the out-of-sample error can be informative about the missing counterfactual outcome if it is mutually or serially correlated.