“Stochastic Independence, Causal Independence, and Shieldability”: The aim of the paper is to explicate causal independence and Reichenbach's screening-off relation, or equivalently, causal dependence and direct causal dependence (always taken as relations between sets of factors or variables) in probabilistic terms along the lines of Suppes' probabilistic theory of causality, which is amended by the observation that there are not only spurious and indirect, but also hidden causes. The notion central to the explication is that of conditional stochastic or probabilistic independence, for which the graphoid axioms, as they have been called later on, are proved to hold. The adequacy of the explication is supported by proving some intuitively e...
. Special conditional independence structures have been recognized to be matroids. This opens new po...
Titans like Bertrand Russell or Karl Pearson warned us to keep ourmathematical and statistical hands...
Estimating the strength of causal effects from observational data is a common problem in scientific ...
“Stochastic Independence, Causal Independence, and Shieldability”: The aim of the paper is to explic...
The paper observes that the theory of causal dependence developed in my paper “Stochastic Independen...
This dissertation studies the definition, identification, and estimation of causal effects within th...
Abstract: As the paper explains, it is crucial to epistemology in general and to the theory of causa...
This work investigates the intersection property of conditional independence. It states that for ran...
Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure le...
The goal of this paper is to integrate the notions of stochastic conditional independence and variat...
Inferring the causal structure that links $n$ observables is usually based upon detecting statistic...
The paper displays the similarity between the theory of probabilistic causation developed by Glymour...
The semigraphoid closure of every couple of CI-statements (CI=conditional independence) is a stochas...
The theory of causal independence is frequently used to facilitate the assessment of the probabilist...
Probabilistic Conditional Independence Structures provides the mathematical description of probabili...
. Special conditional independence structures have been recognized to be matroids. This opens new po...
Titans like Bertrand Russell or Karl Pearson warned us to keep ourmathematical and statistical hands...
Estimating the strength of causal effects from observational data is a common problem in scientific ...
“Stochastic Independence, Causal Independence, and Shieldability”: The aim of the paper is to explic...
The paper observes that the theory of causal dependence developed in my paper “Stochastic Independen...
This dissertation studies the definition, identification, and estimation of causal effects within th...
Abstract: As the paper explains, it is crucial to epistemology in general and to the theory of causa...
This work investigates the intersection property of conditional independence. It states that for ran...
Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure le...
The goal of this paper is to integrate the notions of stochastic conditional independence and variat...
Inferring the causal structure that links $n$ observables is usually based upon detecting statistic...
The paper displays the similarity between the theory of probabilistic causation developed by Glymour...
The semigraphoid closure of every couple of CI-statements (CI=conditional independence) is a stochas...
The theory of causal independence is frequently used to facilitate the assessment of the probabilist...
Probabilistic Conditional Independence Structures provides the mathematical description of probabili...
. Special conditional independence structures have been recognized to be matroids. This opens new po...
Titans like Bertrand Russell or Karl Pearson warned us to keep ourmathematical and statistical hands...
Estimating the strength of causal effects from observational data is a common problem in scientific ...