73 citations · 216 across the 15 of their papers we have counts for
6 papers · 2 filters
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…
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…
Polynomial Constraints in Causal Bayesian Networks
Changsung Kang, Jin Tian
We use the implicitization procedure to generate polynomial equality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesi…
Identifying Dynamic Sequential Plans
Jin Tian
We address the problem of identifying dynamic sequential plans in the framework of causal Bayesian networks, and show that the problem is reduced to identifying causal effects, for…