Political scientists are often interested in estimating causal effects. Identification of causal estimates with observational data invariably requires strong untestable assumptions. Here, we outline a number of the assumptions used in the extant empirical literature. We argue that these assumptions require careful evaluation within the context of specific applications. To that end, we present a...

We tackle the problem of how to use information from multiple (in)dependence models, representing results from different experiments, including background knowledge, in causal discovery. We introduce the framework of a causal system in an external context to derive a connection between strict conditional independencies and causal relations between variables. Constraint-based causal discovery is...

We present a symbolic machinery that admits both probabilistic and causal information about a given domain and produces probabilistic statements about he effect of actions and the impact of observations. Thecalculus admits two types of conditioning operators: ordinary Bayes conditioning, P(y]X = z), which represents he observation X z, and causal conditioning, P(yldo(X = x)), read the probabili...

We propose a method to evaluate the complexity of probability measures from data that is based on a reproducing kernel Hilbert space seminorm of the logarithm of conditional probability densities. The motivation is to provide a tool for a causal inference method which assumes that conditional probabilities for effects given their causes are typically simpler and smoother than vice-versa. We pre...

In an independence model, the triplets that represent conditional independences between singletons are called elementary. It is known that the elementary triplets represent the independence model unambiguously under some conditions. In this paper, we show how this representation helps performing some operations with independence models, such as finding the dominant triplets or a minimal indepen...

This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statistic (KMSS). The KMSS provides a generic solution to inductive inference. It states that we should seek for the minimal model that captures all regularities of the data. The conditional independencies following from the ...

The paper addresses uncertain reasoning based on causal knowledge given by two layered networks, where nodes in one layer express possible causes and those in the other are possible effects. Uncertainties of the causalities are given by conditional causal possibilities, which were proposed to express the exact degrees of possibility of causalities. They also have an advantage over the conventio...

This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, and t...