Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data
Causal knowledge is highly valuable but difficult to discover and hidden from view. This creates a challenge for evaluating causal discovery algorithms. causaLens have developed a flexible framework for generating synthetic data which enables evaluation of time series causal discovery methods. We use our framework to benchmark prominent methods.