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Debating the Value and Limitations of Causal Machine Learning Models in Indian Private Markets
Priya Menon
October 12, 2026
Debate surrounds the use of causal machine learning in Indian private markets. This article presents structured arguments and real anecdotes to guide practitioners’ perspectives.
Thesis
Causal machine learning in private markets delivers unique explanatory power, but practitioners must manage complexity and prioritize transparency for maximum benefit.
Causal models offer unique explanatory power
AdvantageAnecdotes show that, when standard correlation analysis left practitioners with unanswered questions, causal models enabled the identification of hidden drivers. In a notable case, a deal’s surprising performance was traced back to operational decisions, not external macro trends, shifting the focus of due diligence.
Complexity may obscure key findings
LimitationTeams have experienced situations where model complexity led to confusion about what factors actually mattered. In one Indian market case, the inclusion of too many variables resulted in ambiguous outputs and unclear recommendations, prompting a return to simpler, more interpretable models.
Model transparency builds stakeholder trust
AdvantagePractitioners note that models which clearly communicate causal assumptions and drivers enable better decision-making by stakeholders. Anecdotes highlight successful collaboration between analytics teams and deal leads when model outputs were both understandable and actionable.
Conclusion
Causal modeling brings both clarity and complexity to private market analysis in India. Success hinges on achieving balance: models must be transparent and focused to maximize their practical value.