| Identifiability of sparse causal effects using instrumental variables. | 0 | 0.34 | 2022 |
| Exploiting Independent Instruments: Identification and Distribution Generalization. | 0 | 0.34 | 2022 |
| Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables | 0 | 0.34 | 2020 |
| Invariant Models for Causal Transfer Learning. | 2 | 0.36 | 2018 |
| Identifying Causal Structure in Large-Scale Kinetic Systems. | 1 | 0.38 | 2018 |
| Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions. | 0 | 0.34 | 2016 |
| Modeling Confounding By Half-Sibling Regression | 0 | 0.34 | 2016 |
| The Arrow of Time in Multivariate Time Series. | 3 | 0.42 | 2016 |
| BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions | 2 | 0.39 | 2015 |
| Structural intervention distance for evaluating causal graphs. | 6 | 0.54 | 2015 |
| Removing systematic errors for exoplanet search via latent causes | 0 | 0.34 | 2015 |
| Distinguishing cause from effect using observational data: methods and benchmarks. | 2 | 0.37 | 2014 |
| Causal Inference on Time Series using Restricted Structural Equation Models. | 11 | 0.77 | 2013 |
| Counterfactual reasoning and learning systems: the example of computational advertising | 106 | 4.33 | 2013 |
| Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. | 1 | 0.37 | 2013 |
| CAM: Causal Additive Models, high-dimensional order search and penalized regression. | 13 | 0.73 | 2013 |
| Counterfactual Reasoning and Learning Systems | 9 | 0.66 | 2012 |
| Causal Inference on Time Series using Structural Equation Models | 3 | 0.45 | 2012 |
| Identifying confounders using additive noise models | 10 | 0.88 | 2012 |
| On causal and anticausal learning. | 41 | 1.77 | 2012 |
| Robust Learning via Cause-Effect Models | 1 | 0.51 | 2011 |
| Identifiability of Causal Graphs using Functional Models | 18 | 0.97 | 2011 |
| Kernel-based Conditional Independence Test and Application in Causal Discovery | 76 | 3.25 | 2011 |
| Detecting low-complexity unobserved causes | 2 | 0.52 | 2011 |
| Identifying Cause and Effect on Discrete Data using Additive Noise Models | 17 | 1.01 | 2010 |
| Detecting the direction of causal time series | 14 | 1.41 | 2009 |
| Regression by dependence minimization and its application to causal inference in additive noise models | 28 | 1.72 | 2009 |
| Kernel Methods for Detecting the Direction of Time Series | 2 | 0.55 | 2008 |
| Nonlinear causal discovery with additive noise models | 137 | 6.88 | 2008 |
