Arguments for and Against Using Causal Machine Learning Models to Identify Drivers of Private Market Returns
Priya Menon
September 27, 2026
Causal modeling in private markets brings both strengths and weaknesses. This article examines arguments for and against its use, drawing on real stories and data from Indian practitioners.
Thesis
Causal machine learning in Indian private markets presents clear advantages for uncovering meaningful return drivers, but requires careful attention to data quality, model assumptions, and the need for regular review.
Causal models reveal hidden market drivers
AdvantageMarket practitioners recount how standard regression analysis missed key influences that only became clear through the application of causal models. In one case, a fund attributed unexpected return volatility to sector trends, but further causal analysis identified that changes in transaction timing and deal structure were the true contributors. This shift in understanding led to more focused diligence and risk assessment.
Models depend on data quality and assumptions
LimitationAnecdotes from the Indian market highlight how small sample sizes and unobserved variables can distort causal inferences. For instance, incomplete information on portfolio company operations led to false conclusions about policy impact. Practitioners note that careful data validation and skepticism are necessary to avoid overfitting or misattribution.
Continuous review improves model relevance
AdvantageTeams describe using ongoing model testing to adapt to new information. One story details how, after an economic shock, causal models initially failed to predict changes in return patterns. Iterative refinement and integration of new variables improved model accuracy, supporting the case for routine reassessment rather than static analysis.
Conclusion
Causal machine learning offers significant potential for revealing the drivers of private market returns, especially when practitioners acknowledge data limitations and embrace ongoing model refinement. The discipline’s value lies in its ability to evolve alongside market realities.