A Primer on Causal Inference Methods for Financial Time-Series Data

Moving beyond spurious correlation to identify cause-and-effect relationships in financial time-series data.

TL;DR

• While machine learning tends to excel at prediction and pattern recognition tasks, it often struggles greatly in identifying true causal relationships.

• This distinction is critical in quantitative finance, where individuals such as asset managers need to determine whether a strategy’s edge comes from a genuine causal driver (e.g., risk premium) or a spurious correlation.

• For example, while the VIX and total trading volume tend to be strongly correlated, the relationship is likely driven by a third external factor—market uncertainty—that influences both.

• Over the past few years, econometricians have developed several methods for teasing apart causality. In this article we cover three applicable to financial time-series data: difference-in-differences, synthetic control, and causal ARIMA.

Read More: In this article we cover the foundations and methods for causal inference with time series data along with their specific applications in Quantitative Finance.

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