Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions

By Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim. 2019

79 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Amin Jaber
2. Murat Kocaoglu
3. Sébastien Lachapelle
4. Simon Lacoste-Julien
5. Alexandre Lacoste
6. Han-Jia Ye
7. De-chuan Zhan

In Probabilistic & Bayesian Methods

1. Amin Jaber
2. Murat Kocaoglu
3. Sébastien Lachapelle
4. Simon Lacoste-Julien
5. Alexandre Drouin
6. Philippe Brouillard
7. Alexandre Lacoste

Citations in the corpus

• Differentiable Causal Discovery from Interventional Data (2020, methodology): “Methods that support interventional data include COmbINE [45], HEJ [19], which both rely on Boolean satisfiability solvers to find a graph that satisfies all constraints; and [24], which proposes an algorithm inspired by FCI [41].”
• Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning (2020, methodology): “Next, we generalize the interventional Markov property ( I -Markov) [13] for the case when the targets are unknown, which we call Ψ -Markov.”
• D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, unknown): “PC [223] 1993 constraint yes yes yes no CPDAG CCD [196] 1996 constraint yes yes both no PAG FCI [223] 2000 constraint no yes yes no PAG TPDA [31] 2002 constraint yes yes yes no CPDAG CPC [190] 2006 constraint yes relaxed yes no CPDAG KCL [227] 2007 constraint yes yes yes no CPDAG ION [234] 2008 cons”