UvA-DARE (Digital Academic Repository) Methods for causal inference from gene perturbation experiments and validation

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Causal Models for Dynamical Systems (2020, methodology): “This ﬁnding adds to a recent debate suggesting to add invariance as a ﬁtting criteria to data science methodology [Sch¨olkopf et al., 2012, Yu, 2013, Peters et al., 2016, Bareinboim and Pearl, 2016, Meinshausen et al., 2016, Yu and Kumbier, 2019].”
Joint Causal Inference from Multiple Contexts (2016, methodology): “ICP has been successfully applied to predict the effects of gene knockouts on gene expression levels (Meinshausen et al., 2016) from large-scale interventional yeast gene expression data (Kemmeren et al.”
Identifying Best Interventions through Online Importance Sampling (2017, methodology): “, 2013), biological systems (Meinshausen et al., 2016), sociology (Blalock, 1985), agriculture (Splawa-Neyman et al.”
Permutation-Based Causal Structure Learning with Unknown Intervention Targets (2019, methodology): “A different approach to this problem is given by the invariant causal inference framework (Meinshausen et al., 2016; Rothenhäusler et al., 2015; Ghassami et al., 2017).”
Permutation-based Causal Inference Algorithms with Interventions (2017, methodology): “Hence GIES is a parametric approach (relying on Gaussianity) and while it has been applied to real data [8, 9, 15], we will demonstrate via an example in Section 3 that it is in general not consistent.”