Venture Capital (VC) plays a crucial role in promoting entrepreneurship and innovation, enabling start-up firms to access public markets; yet, its impact on Initial Public Offering (IPO) performance remains contested. This study analyzes the impact of VC backing on IPO pricing efficiency, investor demand, and short-term performance after listing across different market regimes in India from 2009 to 2023. Using a comprehensive dataset of 426 IPOs, the study integrates causal machine learning (ML) with econometric modeling to estimate both the average and heterogeneous effects of VC participation. The OLS results indicate that the VC-backed IPOs leave more money on the table and experience greater upward revisions in offer price. However, such IPOs record lower short-term returns after listing. The causal forest analysis further reveals significant heterogeneity in the impact of VC backing, indicating that the certification and monitoring role of VCs is stronger during bearish or high-volatility market regimes when information asymmetry is elevated. Conversely, under bullish market conditions characterized by investor optimism, the incremental value of VC participation declines. The findings indicate that the impact of VC backing is dynamic and market-dependent and underscore the importance of integrating data-driven approaches with econometric techniques in the study of entrepreneurial finance. This study offers valuable insights for entrepreneurs, investors, and policymakers by highlighting how market regime shapes the effectiveness of VC backing on IPO outcomes.