Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming

By Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo. 2014

151 citations recorded by the source corpus.

Subfields: Causal Inference & Discovery, Knowledge Graphs & Representation

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Citations in the corpus

Differentiable Causal Discovery from Interventional Data (2020, methodology): “Results are reported in Tables 16, 17, 18 of Section C.5.2.”
Causal Discovery from Subsampled Time Series Data by Constraint Optimization (2016, methodology): “A more intricate approach is to use pseudo-Boolean weights following Hyttinen et al. (2014); Sonntag et al. (2015); Margaritis and Bromberg (2009). They used Bayesian model selection to obtain reliability weights for independence tests. Instead of a p-value and a binary decision, these types of test”
Permutation-based Causal Inference Algorithms with Interventions (2017, methodology): “Methods such as ACI [13], HEJ [10], COmbINE [25] and ICP [15] allow for latent confounders with possibly unknown interventional targets.”
Permutation-Based Causal Structure Learning with Unknown Intervention Targets (2019, background): “…B¨uhlmann, 2012) and IGSP (Wang et al., 2017; Yang et al., 2018) under the assumption of causal suﬃ-ciency, i.e., when there are no latent confounders, and ACI (Magliacane et al., 2016), HEJ (Hyttinen et al., 2014) and COmbINE (Triantaﬁllou and Tsamardinos, 2015) that allow for latent confounders.”
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions (2018, background): “Methods that allow for latent confounders and unknown intervention targets include Eaton & Murphy (2007), JCI (Magliacane et al., 2016), HEJ (Hyttinen et al., 2014), CombINE (Triantaﬁllou & Tsamardinos, 2015), and ICP (Peters et al., 2016), but they do not have consistency guarantees for returning…”
Causal feature learning: an overview (2017, unknown): “Like the majority of work in causal inference and discovery—notable exceptions being (Richardson 1996; Mooij et al. 2011; Lacerda et al. 2012; Hyttinen et al. 2012, 2014)—CFL assumes that the microvariable system is acyclic: in our toy example, hue has causal influence on eda, but we assumed (quite…”
Generalized Score Functions for Causal Discovery (2018, methodology): “, by using logical encoding of independence constraints [17], to handle conflicts; However, how to determine the weights for different constraints remains an issue.”
Do-calculus when the True Graph Is Unknown (2015, methodology): “We translate the d-separation constraints of the equivalence class into a logical representation using the ASP-encoding of Hyttinen et al. (2014).”
Learning Optimal Causal Graphs with Exact Search (2018, methodology): “…it has been shown that better accuracy can be obtained when a predetermined, larger set of tests are conducted before the actual search, and conflicting test results are resolved in an optimal way via exact methods (Hyttinen et al., 2014; Magliacane et al., 2016; Borboudakis and Tsamardinos, 2016).”
Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints (2017, methodology): “This is similar to scoring a DAG that consists of the following edges: Xi ← Zi → Yi and Xi → Yi, which has been used previously [20].”
Core-Boosted Linear Search for Incomplete MaxSAT (2019, unknown): “xml (middle) [22], causal-discovery-causal carpo 8 100 (right) [26].”
Joint Causal Inference from Multiple Contexts (2016, methodology): “The latter approach is commonly used by constraint-based methods for causal discovery from multiple data sets (e.g., Tillman, 2009; Claassen and Heskes, 2010; Tillman and Spirtes, 2011; Hyttinen et al., 2014; Triantafillou and Tsamardinos, 2015; Rothenhäusler et al., 2015; Forré and Mooij, 2018).”
Causal Transfer Learning (2017, methodology): “14 To test the separating set condition (2), we use the approach proposed by Hyttinen et al. [2014], where we simply add the background knowledge (Assumption 1) concerning the non-deterministic JCI setting as constraints on the optimization problem, in addition to the transfer-learning specific assu”