Results 41 to 50 of about 117 (76)

Personalized decision making – A conceptual introduction

Journal of Causal Inference, 2023
Personalized decision making targets the behavior of a specific individual, while population-based decision making concerns a subpopulation resembling that individual.
Mueller Scott, Pearl Judea
doaj   +1 more source

Causality of Functional Longitudinal Data

, 2023
"Treatment-confounder feedback" is the central complication to resolve in longitudinal studies, to infer causality. The existing frameworks for identifying causal effects for longitudinal studies with discrete repeated measures hinge heavily on assuming ...
Ying, Andrew
core  

Rate doubly robust estimation for weighted average treatment effects

Journal of Causal Inference
The weighted average treatment effect (WATE) defines a versatile class of causal estimands for populations characterized by propensity score weights, including the average treatment effect (ATE), treatment effect on the treated (ATT), on controls (ATC ...
Wang Yiming, Liu Yi, Yang Shu
doaj   +1 more source

Quantitative probing: Validating causal models with quantitative domain knowledge

Journal of Causal Inference, 2023
We propose quantitative probing as a model-agnostic framework for validating causal models in the presence of quantitative domain knowledge. The method is constructed in analogy to the train/test split in correlation-based machine learning.
Grünbaum Daniel, Stern Maike L., Lang Elmar W.   +2 more
doaj   +1 more source

Greedy Causal Discovery is Geometric

, 2021
Finding a directed acyclic graph (DAG) that best encodes the conditional independence statements observable from data is a central question within causality.
Linusson, Svante, Restadh, Petter, Solus, Liam   +2 more
core  

Adding covariates to bounds: what is the question?

Journal of Causal Inference
Symbolic nonparametric bounds for partial identification of causal effects now have a long history in the causal literature. Sharp bounds, bounds that use all available information to make the range of values as narrow as possible, are often the goal ...
Jonzon Gustav, Gabriel Erin E., Sjölander Arvid, Sachs Michael C.   +3 more
doaj   +1 more source

Representation of Context-Specific Causal Models with Observational and Interventional Data

, 2022
We consider the problem of representing causal models that encode context-specific information for discrete data using a proper subclass of staged tree models which we call CStrees. We show that the context-specific information encoded by a CStree can be
Duarte, Eliana, Solus, Liam
core  

Heavy-tailed max-linear structural equation models in networks with hidden nodes

, 2023
Recursive max-linear vectors provide models for the causal dependence between large values of observed random variables as they are supported on directed acyclic graphs (DAGs).
Davison, Anthony C., Klüppelberg, Claudia, Krali, Mario   +2 more
core  

Treatment effect estimation with observational network data using machine learning

Journal of Causal Inference
Causal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in spillover effects between them.
Emmenegger Corinne, Spohn Meta-Lina, Elmer Timon, Bühlmann Peter   +3 more
doaj   +1 more source

Seemingly unrelated Bayesian additive regression trees for cost-effectiveness analyses in healthcare [PDF]

In recent years, theoretical results and simulation evidence have shown Bayesian additive regression trees to be a highly-effective method for nonparametric regression.
Bosmans, Judith, Esser, Jonas, Klausch, Thomas, Maia, Mateus, Murphy, Keefe, Parnell, Andrew C., van Dongen, Hanneke   +6 more
core   +2 more sources