73 citations · 118 across the 6 of their papers we have counts for
6 papers · 1 filter
Probabilities of Causation: Bounds and Identification
Jin Tian, Judea Pearl
This paper deals with the problem of estimating the probability that one event was a cause of another in a given scenario. Using structural-semantical definitions of the probabilit…
A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks
Jin Tian
This paper extends the work in [Suzuki, 1996] and presents an efficient depth-first branch-and-bound algorithm for learning Bayesian network structures, based on the minimum descri…
Causal Discovery from Changes
Jin Tian, Judea Pearl
We propose a new method of discovering causal structures, based on the detection of local, spontaneous changes in the underlying data-generating model. We analyze the classes of st…
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…