Results 21 to 30 of about 158,058 (264)

Invariant Causal Prediction for Nonlinear Models

Journal of Causal Inference, 2018
An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system’s underlying causal structure.
Heinze-Deml Christina, Peters Jonas, Meinshausen Nicolai   +2 more
doaj   +1 more source

Structure Mapping for Transferability of Causal Models

CoRR, 2020
Presented at the Inductive Biases, Invariances and Generalization in Reinforcement Learning Workshop, ICML ...
Purva Pruthi, Javier González 0002, Xiaoyu Lu, Madalina Fiterau   +3 more
openaire   +2 more sources

Marginal Structural Models to Estimate Causal Effects of Right-to-Carry Laws on Crime

Statistics and Public Policy, 2022
Right-to-carry (RTC) laws allow the legal carrying of concealed firearms for defense, in certain states in the United States. I used modern causal inference methodology from epidemiology to examine the effect of RTC laws on crime over a period from 1959 ...
Willem M. Van Der Wal
doaj   +1 more source

Causal Discovery in Linear Structural Causal Models with Deterministic Relations

CoRR, 2021
Accepted at 1st Conference on Causal Learning and Reasoning (CLeaR 2022)
Yuqin Yang, Mohamed S. Nafea, AmirEmad Ghassami, Negar Kiyavash   +3 more
openaire   +3 more sources

249 Addressing Structural Racism Using Community Based System Dynamics

Journal of Clinical and Translational Science, 2023
OBJECTIVES/GOALS: 1. Describe method of Community Based System Dynamics. 2. Describe CBSD as used in addressing structural racism in a previously redlined community. 3.
Heidi Gullett, Gregory Brown, Delores Collins, Michael Mathews, Kurt Stange, Peter Hovmand   +5 more
doaj   +1 more source

Causal Bandits for Linear Structural Equation Models

J. Mach. Learn. Res., 2022
61 pages; new to this version: added lower bounds and relaxed ...
Burak Varici, Karthikeyan Shanmugam 0001, Prasanna Sattigeri, Ali Tajer   +3 more
openaire   +4 more sources

A Critical View of the Structural Causal Model

CoRR, 2020
In the univariate case, we show that by comparing the individual complexities of univariate cause and effect, one can identify the cause and the effect, without considering their interaction at all. In our framework, complexities are captured by the reconstruction error of an autoencoder that operates on the quantiles of the distribution. Comparing the
Tomer Galanti, Ofir Nabati, Lior Wolf
openaire   +2 more sources

Causality and tractable probabilistic models

Frontiers in Computer Science
Causal assertions stem from an asymmetric relation between some variable's causes and effects, i.e., they imply the existence of a function decomposition of a model where the effects are a function of the causes without implying that the causes are ...
David Cruz, Jorge Batista, Jorge Batista
doaj   +1 more source

Population-Level Effectiveness of COVID-19 Vaccination Program in the United States: Causal Analysis Based on Structural Nested Mean Model

Vaccines, 2022
Though COVID-19 vaccines have shown high efficacy, real-world effectiveness at the population level remains unclear. Based on the longitudinal data on vaccination coverage and daily infection cases from fifty states in the United States from March to May
Rui Wang, Jiahao Wang, Taojun Hu, Xiao-Hua Zhou   +3 more
doaj   +1 more source

Structural Causal Bottleneck Models

CoRR
We introduce structural causal bottleneck models (SCBMs), a novel class of structural causal models. At the core of SCBMs lies the assumption that causal effects between high-dimensional variables only depend on low-dimensional summary statistics, or bottlenecks, of the causes.
Simon Bing, Jonas Wahl, Jakob Runge
openaire   +2 more sources