DeepAI AI Chat
Log In Sign Up

Orthogonal Structure Search for Efficient Causal Discovery from Observational Data

by   Anant Raj, et al.

The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines. Recent work exploits stability of regression coefficients or invariance properties of models across different experimental conditions for reconstructing the full causal graph. These approaches generally do not scale well with the number of the explanatory variables and are difficult to extend to nonlinear relationships. Contrary to existing work, we propose an approach which even works for observational data alone, while still offering theoretical guarantees including the case of partially nonlinear relationships. Our algorithm requires only one estimation for each variable and in our experiments we apply our causal discovery algorithm even to large graphs, demonstrating significant improvements compared to well established approaches.


page 1

page 2

page 3

page 4


Causal Feature Selection via Orthogonal Search

The problem of inferring the direct causal parents of a response variabl...

Unsuitability of NOTEARS for Causal Graph Discovery

Causal Discovery methods aim to identify a DAG structure that represents...

Typing assumptions improve identification in causal discovery

Causal discovery from observational data is a challenging task to which ...

Reconstructing regime-dependent causal relationships from observational time series

Inferring causal relations from observational time series data is a key ...

Nonlinear Causal Discovery via Kernel Anchor Regression

Learning causal relationships is a fundamental problem in science. Ancho...

Direct Estimation of Difference Between Structural Equation Models in High Dimensions

Discovering cause-effect relationships between variables from observatio...

Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation

Simulations are ubiquitous in machine learning. Especially in graph lear...