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.

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causaLens’ Andrew Lawrence, Marcus Kaiser, Rui Sampaio, and Maksim Sipos introduce a framework for evaluating causal discovery techniques for time-series data.

Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data

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