73 citations · 216 across the 15 of their papers we have counts for
10 papers · 1 filter
On the Testable Implications of Causal Models with Hidden Variables
Jin Tian, Judea Pearl
The validity OF a causal model can be tested ONLY IF the model imposes constraints ON the probability distribution that governs the generated data. IN the presence OF unmeasured va…
Identifying Conditional Causal Effects
Jin Tian
This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in wh…
Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement
Jin Tian
Maximal ancestral graphs (MAGs) are used to encode conditional independence relations in DAG models with hidden variables. Different MAGs may represent the same set of conditional…
Local Markov Property for Models Satisfying Composition Axiom
Changsung Kang, Jin Tian
The local Markov condition for a DAG to be an independence map of a probability distribution is well known. For DAGs with latent variables, represented as bi-directed edges in the…
Inequality Constraints in Causal Models with Hidden Variables
Changsung Kang, Jin Tian
We present a class of inequality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network, in which some of the var…
A Criterion for Parameter Identification in Structural Equation Models
Jin Tian
This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. The paper establishes a sufficient criterion for identifying…