Zobrazeno 1 - 10
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pro vyhledávání: '"Peña, Jose M."'
Autor:
Peña, Jose M.
We report assumption-free bounds for any contrast between the probabilities of the potential outcome under exposure and non-exposure when the confounders are missing not at random. We assume that the missingness mechanism is outcome-independent. We a
Externí odkaz:
http://arxiv.org/abs/2410.06726
Autor:
Peña, Jose M.
We extend our previous work on sensitivity analysis for the risk ratio and difference contrasts under unmeasured confounding to any contrast. We prove that the bounds produced are still arbitrarily sharp, i.e. practically attainable. We illustrate th
Externí odkaz:
http://arxiv.org/abs/2406.07940
Explaining social events is a primary objective of applied data-driven sociology. To achieve that objective, many sociologists use statistical causal inference to identify causality using observational studies research context where the analyst does
Externí odkaz:
http://arxiv.org/abs/2311.13410
Autor:
Peña, Jose M.
This work is devoted to the study of the probability of immunity, i.e. the effect occurs whether exposed or not. We derive necessary and sufficient conditions for non-immunity and $\epsilon$-bounded immunity, i.e. the probability of immunity is zero
Externí odkaz:
http://arxiv.org/abs/2309.11942
Autor:
Peña, Jose M.
There are a number of measures of direct and indirect effects in the literature. They are suitable in some cases and unsuitable in others. We describe a case where the existing measures are unsuitable and propose new suitable ones. We also show that
Externí odkaz:
http://arxiv.org/abs/2306.01292
Autor:
Peña, Jose M.
We present two methods for bounding the probabilities of benefit and harm under unmeasured confounding. The first method computes the (upper or lower) bound of either probability as a function of the observed data distribution and two intuitive sensi
Externí odkaz:
http://arxiv.org/abs/2303.05396
We propose a novel sensitivity analysis to unobserved confounding in observational studies using copulas and normalizing flows. Using the idea of interventional equivalence of structural causal models, we develop $\rho$-GNF ($\rho$-graphical normaliz
Externí odkaz:
http://arxiv.org/abs/2209.07111
This work demonstrates the application of a particular branch of causal inference and deep learning models: \emph{causal-Graphical Normalizing Flows (c-GNFs)}. In a recent contribution, scholars showed that normalizing flows carry certain properties,
Externí odkaz:
http://arxiv.org/abs/2202.09391
Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average causal effect (ACE) and conditional ACE (CACE). Traditional causal effect estimation methods such as Inverse Probabi
Externí odkaz:
http://arxiv.org/abs/2202.03281
Autor:
Belissent, Nicolas, Peña, José M., Mesías-Ruiz, Gustavo A., Shawe-Taylor, John, Pérez-Ortiz, María
Publikováno v:
In Knowledge-Based Systems 23 May 2024 292
