Identifying and Addressing Hidden Risks in Causal Machine Learning for Private Markets in India
Causal machine learning uncovers new risks and requires vigilance from practitioners working in evolving Indian private markets.
Hidden risks lurk within causal modeling, especially in the context of Indian private markets. This article shares stories of overlooked pitfalls and the practices that help mitigate them.
Small Samples: Cautionary Tales
Anecdotes detail how teams initially overlooked the risks associated with small sample sizes in private market data. In several cases, models suggested strong causal links that later failed to hold up when more data became available. Practitioners learned to treat findings as provisional and to view sample size as a critical element in assessing model reliability.
Volatility Undermines Assumptions
Rapid shifts in the Indian economic landscape revealed the danger of static modeling assumptions. Anecdotes describe deals where sudden policy or economic changes rendered prior causal conclusions obsolete. The lesson: ongoing vigilance and model revision are essential for meaningful insights.
Feedback Loops and Model Resilience
Teams report that the most durable insights came from models subjected to frequent review and adjustment. One group described how regular feedback sessions surfaced errors that would have gone undetected. These stories reinforce the need for an iterative, feedback-driven approach.