Analysis
Photo Essay: How Indian Private Market Practitioners Use Causal Machine Learning to Rethink Transaction Outcomes
Priya Menon
September 20, 2026
Anecdotes from Indian private markets often feature transactions where initial analysis failed to reveal the true drivers of return. Only after applying causal modeling did teams uncover overlooked variables.
A data science team in Bengaluru opens a new transaction file. At first glance, the numbers suggest a strong correlation between regional macroeconomic growth and deal returns. However, a closer look using causal modeling techniques uncovers that the timing of capital deployment and changes in deal structure have an outsized effect, prompting a shift in focus away from surface-level economic trends.
A senior partner recounts how an apparent surge in returns was quickly attributed to favorable government policy. Yet, further scrutiny through a causal lens revealed that operational improvements within portfolio companies, rather than external policy, played the more decisive role. The anecdote underscores the risk of attributing results to high-profile news instead of verified drivers.
In a Mumbai workshop, teams compare model outcomes. The original expectation was that sector choice would dominate return outcomes. After testing multiple hypotheses, causal analysis points instead to differences in transaction timing and diligence depth. The ongoing process of challenging assumptions is reinforced as an essential discipline in market research.
Anecdotes and data-driven reviews confirm that each new dataset or transaction invites further scrutiny. The discipline of causal modeling is a process of continual learning, never a one-time event.