Results 11 to 20 of about 44 (37)

Inference for copula modeling of discrete data: a cautionary tale and some facts

Dependence Modeling, 2017
In this note, we elucidate some of the mathematical, statistical and epistemological issues involved in using copulas to model discrete data. We contrast the possible use of (nonparametric) copula methods versus the problematic use of parametric copula ...
Faugeras Olivier P.
doaj   +1 more source

Causal inference with textual data: A quasi-experimental design assessing the association between author metadata and acceptance among ICLR submissions from 2017 to 2022

Journal of Causal Inference
Many recent studies have probed status bias in the peer-review process of academic journals and conferences. In this article, we investigated the association between author metadata and area chairs’ final decisions (Accept/Reject) using our compiled ...
Chen Chang, Zhang Jiayao, Ye Ting, Roth Dan, Zhang Bo   +4 more
doaj   +1 more source

Potential outcomes and decision-theoretic foundations for statistical causality: Response to Richardson and Robins

Journal of Causal Inference
I thank Thomas Richardson and James Robins for their discussion of my article, and discuss the similarities and differences between their approach to causal modelling, based on single world intervention graphs, and my own decision-theoretic approach.
Dawid Philip
doaj   +1 more source

Decision making, symmetry and structure: Justifying causal interventions

Journal of Causal Inference
We can use structural causal models (SCMs) to help us evaluate the consequences of actions given data. SCMs identify actions with structural interventions. A careful decision maker may wonder whether this identification is justified.
Johnston David O., Ong Cheng Soon, Williamson Robert C.   +2 more
doaj   +1 more source

From urn models to box models: Making Neyman's (1923) insights accessible

Journal of Causal Inference
Neyman’s 1923 paper introduced the potential outcomes framework and the foundations of randomization-based inference. We discuss the influence of Neyman’s paper on four introductory to intermediate-level textbooks by Berkeley faculty members (Scheffé ...
Lin Winston, Dudoit Sandrine, Nolan Deborah, Speed Terence P.   +3 more
doaj   +1 more source

Idescat. SORT. On the frequentist and Bayesian approaches to hypothesis testing. Volume 30 (1)

, 2020
Hypothesis testing is a model selection problem for which the solution proposed by the two main statistical streams of thought, frequentists and Bayesians, substantially differ.
Elías Moreno, Girón, F Javier Girón, F Javier   +3 more
core  

Spillover detection for donor selection in synthetic control models

Journal of Causal Inference
Synthetic control (SC) models are widely used to estimate causal effects in settings with observational time-series data. To identify the causal effect on a target unit, SC requires the existence of additional units that are not impacted by the ...
O’Riordan Michael, Gilligan-Lee Ciarán M.   +1 more
doaj   +1 more source

Unfair utility functions and first steps towards improving them

Journal of Causal Inference
Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the policy under such constraints.
Jørgensen Frederik Hytting, Weichwald Sebastian, Peters Jonas   +2 more
doaj   +1 more source

A Characterization for the Spherical Scoring Rule

decision theory, probability assessment, probability elicitation, proportionality, strictly proper scoring rule, Primary 62C99, Secondary 62A01, 90B50, 91B06, C44, C89,
Victor Jose
core   +1 more source

Comment on: “Decision-theoretic foundations for statistical causality”

Journal of Causal Inference, 2022
Shpitser Ilya
doaj   +1 more source