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- Jin Tian, Judea Pearl
- AAAI/IAAI
- 2002

This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive assumptions. The assumptions are encoded inâ€¦ (More)

This paper concerns the assessment of the effects of actions or policies from a combination of: (i) nonexperimental data, and (ii) causal assumptions. The assumptions are encoded in the form of aâ€¦ (More)

- Jin Tian, Judea Pearl
- UAI
- 2002

The validity of a causal model can be tested only if the model imposes constraints on the probability distribution that governs the genÂ erated data. In the presence of unmeasured variables, causalâ€¦ (More)

- Jin Tian
- UAI
- 2000

This paper extends the work in [Suzuki, 1996] and presents an efficient depth-first branchÂ and-bound algorithm for learning Bayesian network structures, based on the minimum description length (MDL)â€¦ (More)

- Jin Tian, Ru He, Lavanya Ram
- UAI
- 2010

Bayesian networks are being widely used in various data mining tasks for probabilistic inference and causual modeling [Pearl (2000), Spirtes et al. (2001)]. Learning the best Bayesian networkâ€¦ (More)

- Jin Tian, Judea Pearl
- UAI
- 2001

We propose a new method of discovering causal structures, based on the detection of local, spontaneous changes in the unÂ derlying data-generating model. We anaÂ lyze the classes of structures thatâ€¦ (More)

We study the problem of learning Bayesian network structures from data. Koivisto and Sood (2004) and Koivisto (2006) presented algorithms that can compute the exact marginal posterior probability ofâ€¦ (More)

- Jin Tian
- AAAI
- 2005

This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. Using techniques developed for graphical causal models, we show that a model canâ€¦ (More)

- Jin Tian, Judea Pearl
- Annals of Mathematics and Artificial Intelligence
- 2000

This paper deals with the problem of estimating the probability of causation, that is, the probability that one event was the real cause of another, in a given scenario. Starting fromâ€¦ (More)

- Elias Bareinboim, Jin Tian, Judea Pearl
- AAAI
- 2014

Selection bias is caused by preferential exclusion of units from the samples and represents a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experimentsâ€¦ (More)