In recent years, there has been a burst of innovative work on methods for estimating causal effects using observational data. Much of this work has extended and brought a renewed focus on old approaches such as matching, which is the focus of this review. The new developments highlight an old tension in the social sciences: a focus on research design versus a focus on quantitative models. This ...

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Literal acceptance of the results of fitting "causal" models to correlational data can lead to conclusions that are of questionable value. The long-established principles of scientific inference must still be applied. In particular, the possible influence of variables that are not observed must be considered; the well-known difference between correlation and causation is still relevant, even wh...

This paper presents methods for modeling and assisting students’ understanding about causalities between physical quantities based on comparative reasoning. Our tutoring system discusses causalities in an object system with the student by choosing dialogue strategies according to student’s understanding states. The student’s understanding state is represented by the causal network and table. Di...

In the study of complex systems one of the major concerns is the detection and characterization of causal interdependencies and couplings between different subsystems. The nature of such dependencies is typically not only nonlinear but also asymmetric and thus makes the use of symmetric and linear methods ineffective. Moreover, signals sampled from real world systems are noisy and short, posing...

This study proposes a novel method to provide unbiased effect estimates in the presence of time-dependent confounding and applies the method in an existing merged multi-source nursing home dataset to examine the effect of antipsychotic medication use on all cause mortality. Using standard methods, effect estimates of time-varying exposures from observational data will be biased in the presence ...

This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pretreatment characteristics. We discuss the us...

Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data. We prove that if the true causal network might be an arbitrary, linear Gaussian network or a discrete Bayes network, then every unambiguous causal conclusion produced by a consistent method from non-experimental data is subject to reversal as the sample size increases...