This article explores the combined application of inductive learning algorithms and causal inference techniques to the problem of discovering causal rules among the attributes of a relational database. Given some relational data, each field can be considered as a random variable, and a hybrid graph can be built by detecting conditional independencies among variables. The induced graph represent...

In this paper we present a new approach to reasoning about actions and causation which is based on a conditional logic. The conditional implication is interpreted as causal implication. This makes it possible to formalize in a uniform way causal dependencies between actions and their immediate and indirect effects. The proposed approach also provides a natural formalization of concurrent action...

This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The theory unifies the graphical, potential-outcome (NeymanRubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper...

One of the most fundamental problems in causal inference is the estimation of a causal effect when treatment and outcome are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploits observational conditional independencies to suggest “weak” pa...

We hypothesized that causal conditional reasoning reflects judgment of the conditional likelihood of causes and effects based on a probabilistic causal model of the scenario being judged. Although this proposal has much in common with Cummins’ (1995) theory based on the number of disabling conditions and alternative causes, it takes more variables into account and therefore makes some differing...

We study the structural identification of causal effects with conditioning instruments within the settable system framework. In particular, we provide causal and predictive conditions sufficient for conditional exogeneity to hold. We provide two procedures based on “exclusive of A” (~A)-causality matrices and the direct causality matrix for inferring conditional causal isolation among vectors o...

Instrumental variables (IVs) are widely used to identify causal effects. For this purpose IVs have to be exogenous, i.e., causally unrelated to all variables in the model except the explanatory variable X . It can be hard to find such variables. A generalized IV method has been proposed that only requires exogeneity conditional on a set of covariates. This leads to a wider choice of potential I...

Causal reasoning has been shown to underlie many aspects of everyday judgment and decision-making. We explore the role of causal structure in conditional reasoning, hypothesizing that people often interpret conditional statements as assertions about causal structure. We argue that responses on the Wason selection task reflect the selection of evidence expected to maximally reduce uncertainty ov...

Least squares regression estimates of causal effects are conditional-variance-weighted estimates of individual-level causal effects. In this paper, we extract from the literature on counterfactual causality a simple nine-step routine to determine whether or not the implicit weighting of regression has generated a misleading estimate of the average causal effect. The diagnostic routine is presen...

Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption The assumption that the assignment to treatments is ignorable conditional on attributes plays an important role in the applied statistic and econometric evaluation literature. Another term for it is conditional independence assumption. This paper discusses identification when the...