10 citations · 44 across the 12 of their papers we have counts for
13 papers
Latent Causal Diffusions for Single-Cell Perturbation Modeling
Lars Lorch, Jiaqi Zhang, Charlotte Bunne +3
Perturbation screens hold the potential to systematically map regulatory processes at single-cell resolution, yet modeling and predicting transcriptome-wide responses to perturbati…
Generative Intervention Models for Causal Perturbation Modeling
Nora Schneider, Lars Lorch, Niki Kilbertus +2
We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external…
Standardizing Structural Causal Models
Weronika Ormaniec, Scott Sussex, Lars Lorch +2
Synthetic datasets generated by structural causal models (SCMs) are commonly used for benchmarking causal structure learning algorithms. However, the variances and pairwise correla…
Causal Modeling with Stationary Diffusions
Lars Lorch, Andreas Krause, Bernhard Schölkopf
We develop a novel approach towards causal inference. Rather than structural equations over a causal graph, we learn stochastic differential equations (SDEs) whose stationary densi…
Active Bayesian Causal Inference
Christian Toth, Lars Lorch, Christian Knoll +4
Causal discovery and causal reasoning are classically treated as separate and consecutive tasks: one first infers the causal graph, and then uses it to estimate causal effects of i…
BaCaDI: Bayesian Causal Discovery with Unknown Interventions
Alexander Hägele, Jonas Rothfuss, Lars Lorch +3
Inferring causal structures from experimentation is a central task in many domains. For example, in biology, recent advances allow us to obtain single-cell expression data under mu…