Seven Habits of Causal Factor Investing

How can investors avoid out-of-sample factor investing strategy failures driven by use of non-causal research methods? In their May 2025 paper entitled “A Protocol for Causal Factor Investing”, Marcos Lopez de Prado and Vincent Zoonekynd introduce the concept of the factor mirage, a factor model that appears statistically valid but is causally mis-specified. They then provide a practical 7-step protocol for causal factor investing that exploits advances in econometrics and machine learning. Based on theoretical analysis, they find that:

• Collider and confounder biases embedded in standard regressions can generate misleading inferences, poor out-of-sample performance and bad investment decisions.
  • Colliders are variables that are causally downstream for both an independent variable and the dependent variable.
    • They often change the sign of coefficients, inducing investors to buy (sell) when they should sell (buy).
    • They are particularly dangerous for standard two-pass or three-pass regressions.
    • Standard evaluation metrics such as adjusted R-squared and t-statistic may reward misspecification and penalize simplicity even when extra variables introduce collider bias.
  • Confounders are uncontrolled variables that cause both an independent variable and the dependent variable. For example, if leverage influences both book-to-market and returns, and is ignored, the estimated role of book-to-market may be biased in magnitude and sign.
• The seven steps for proper causal factor investing are:

• 1. Identify variables potentially associated with future returns.
  2. Create a causal graph that captures variable interdependencies.
  3. Apply do-calculus to the graph to select appropriate control variables.
  4. Use double machine learning to identify causal effects. 
  5. Construct a portfolio that reflects resulting return and risk forecasts.
  6. Backtest portfolio performance via: (a) walk-forward testing; (b) resampling; and, (c) Monte Carlo simulation.
  7. Correct for the elevated risk of false discoveries due to multiple hypothesis testing through p-value and Sharpe ratio adjustments

In summary, investors can produce robust factor investing strategies by using causal reasoning in strategy development.

The paper includes a due diligence questionnaire to assess whether a factor investing scheme is based on causal reasoning.

Cautions regarding findings include:

• The study does not demonstrate/quantify performance improvements in factor investing strategies attributable to use of causal reasoning.
• The methodology described is beyond the reach of most investors, who would bear administrative costs and management fees for delegating to an expert fund manager. Use of the methodology may be costly.

For related research, see results of this search. See also the Compendium of Live ETF Factor/Niche Premium Capture Tests.