This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining biasfree effect estimates in such models.

In this paper, we show how different behavioral consistency concepts can be formed for sequence diagrams and statecharts. Our approach relies on the partial translation of models into a semantic domain and on the definition of explicit consistency conditions. Partial translations and consistency conditions can be combined to form an explicit consistency concept. In order to make our approach ap...

Challenging the contention that statistical methods applied to large numbers of cases invariably provide better grounds for causal inference, this article explores the value of a method of systematic process analysis that can be applied in a small number of cases. It distinguishes among three modes of explanation – historically specific, multivariate and theoryoriented – and argues that systema...

The convenient access to copious multifaceted data has encouraged machine learning researchers reconsider correlation-based and embrace the opportunity of causality-based learning, i.e., causal (causal learning). Recent years have, therefore, witnessed great effort in developing algorithms aiming help artificial intelligence (AI) achieve human-level intelligence. Due lack ground-truth data, one...

In this paper we study methods for estimating causal effects in settings with panel data, where some units are exposed to a treatment during periods and the goal is counterfactual (untreated) outcomes treated unit/period combinations. We propose class of matrix completion estimators that uses observed elements control corresponding untreated unit/periods impute "missing" outcome matrix, units/p...