Treatment Effects in Strategic Management: With an Application to Choosing Early Stage Venture Capital

Abstract

This paper uses the Rubin Causal Model to formalize the treatment effects of a firm choice on its performance. Building from Porter, a firm choice can shape profitability through both strategy and operational effectiveness, but they are distinct in how they do so. The strategic treatment effect is the benefit that is predictable from a firm's characteristics (i.e., resources) and their joint configuration. The strategic determinant function is a mapping of resources to treatment effects, and the role of resource interactions in it determines the importance of coherence for a strategy. Under unconfoundedness, the strategic treatment effect, strategic determinant function, and coherence can be estimated in high-dimensional observational data using machine learning. I present an application estimating the gains from choosing venture capital as early stage financing versus other forms of capital. The results highlight the advantage of considering strategic benefits in this choice. For equity outcomes, there is no average treatment effect of early stage VC, but there is significant heterogeneity: some entrepreneurs can benefit substantially from raising early stage VC, while others be negatively affected. This heterogeneity is predictable from founder, industry and location characteristics. The estimated role of coherence in this choice is moderate. The formalizations in this paper also show that several additional assumptions are required when assessing strategic benefits compared to the usual causal inference. R code to replicate these functions will be included.