Joint Causal Inference from Multiple Contexts

By Joris M. Mooij, Sara Magliacane, Tom Claassen. 2016

300 citations recorded by the source corpus.

How researchers see this paper

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

In Causal Inference & Discovery

Citations in the corpus

Bayesian Structure Learning with Generative Flow Networks (2022, background): “However, it should be noted that this “consensus graph” may not represent a realistic and complete description of the system being modeled here (Mooij et al., 2020).”
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA (2021, background): “…has been explored previously in the context of causal discovery where the intervention occurs on observed variables instead of latent variables like in our case (Eaton and Murphy, 2007; Mooij et al., 2020; Squires et al., 2020; Jaber et al., 2020; Brouillard et al., 2020; Ke et al., 2019).”
Learning Neural Causal Models from Unknown Interventions (2019, background): “A few works have attempted to learn structures from unknown-intervention data [10, 11, 12, 13].”
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions (2017, methodology): “To test whether the invariance condition is satisfied, we apply the recently proposed Joint Causal Inference (JCI) framework [Mooij et al., 2018] to exploit the information provided by multiple domains corresponding to different interventions.”
DAG-GNN: DAG Structure Learning with Graph Neural Networks (2019, methodology): “, 2003), and apply constraintbased methods to multiple environments (Mooij et al., 2016).”
Foundations of structural causal models with cycles and latent variables (2016, methodology): “causal discovery algorithms that have been designed for the acyclic case also apply to simple SCMs with no or only minor changes [41, 44].”
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders (2018, unknown): “In this work, we restricted attention to (stochastic) perfect (“surgical”) interventions, but a straightforward extension to deal with other types of interventions and to generalize the idea of randomized controlled trials can be obtained by applying the JCI framework [23].”
Permutation-Based Causal Structure Learning with Unknown Intervention Targets (2019, methodology): “Recent work (Mooij et al., 2016) introduces a framework for causal structure learning using data from heterogeneous “contexts”, including data from different interventions, to which we will limit our discussion.”
Differentiable Causal Discovery Under Latent Interventions (2022, background): “…include: (i) the causal sufﬁciency assumption may not hold, (ii) the interventions may not be as speciﬁc as stated, and (iii) the ground truth network is possibly not a DAG, since feedback loops are common in cellular signaling networks as noted by Mooij et al. (2020); Brouillard et al. (2020).”
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, background): “We now briefly provide some examples of structural asymmetries, and direct interested readers to Mooij et al. (2016) [166] for a detailed review.”