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Methods and tools for causal discovery and causal inference
Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
Amortized causal discovery: Learning to infer causal graphs from time-series data
On time-series data, most causal discovery methods fit a new model whenever they
encounter samples from a new underlying causal graph. However, these samples often …
encounter samples from a new underlying causal graph. However, these samples often …
Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding
Understanding and quantifying cause and effect relationships is an important problem in
many domains. The generally-agreed standard solution to this problem is to perform a …
many domains. The generally-agreed standard solution to this problem is to perform a …
Causal discovery with multi-domain LiNGAM for latent factors
Y Zeng, S Shimizu, R Cai, F ** Variable Sets
Inferring causal structures from experimentation is a challenging task in many fields. Most
causal structure learning algorithms with unknown interventions are proposed to discover …
causal structure learning algorithms with unknown interventions are proposed to discover …
Bivariate causal discovery via conditional divergence
Telling apart cause and effect is a fundamental problem across many science disciplines.
However, the randomized controlled trial, which is the golden-standard solution for this, is …
However, the randomized controlled trial, which is the golden-standard solution for this, is …
Disentangling causal effects from sets of interventions in the presence of unobserved confounders
The ability to answer causal questions is crucial in many domains, as causal inference
allows one to understand the impact of interventions. In many applications, only a single …
allows one to understand the impact of interventions. In many applications, only a single …
Non-parametric identifiability and sensitivity analysis of synthetic control models
Quantifying cause and effect relationships is an important problem in many domains, from
medicine to economics. The gold standard solution to this problem is to conduct a …
medicine to economics. The gold standard solution to this problem is to conduct a …
Cross-validating causal discovery via Leave-One-Variable-Out
We propose a new approach to falsify causal discovery algorithms without ground truth,
which is based on testing the causal model on a pair of variables that has been dropped …
which is based on testing the causal model on a pair of variables that has been dropped …
Causal Discovery with Hidden Variables Based on Non-Gaussianity and Nonlinearity
A central problem of science is to elucidate the causal mechanisms underlying natural
phenomena and human behavior. Statistical causal inference offers various tools to study …
phenomena and human behavior. Statistical causal inference offers various tools to study …