Is Causality Necessary for Efficient Portfolios? A Computational Perspective on Predictive Validity and Model Misspecification

Recent claims in financial modeling argue that causal identifiability is necessary for valid portfolio optimization, asserting that misspecified models lead to signal inversion and degraded performance. This paper challenges that assertion by showing that structurally misspecified predictive models, those omitting confounders or misrepresenting functional relationships, can still support robust and efficient portfolio construction. We develop a theoretical framework linking optimization performance to the directional alignment between predictive signals and true expected returns, independent of structural correctness. We derive sufficient conditions under which even non-causal signals yield valid, convex efficient frontiers, and we show that the Sharpe ratio scales linearly with alignment. Analytical results are supported by simulation studies demonstrating robustness to confounding and miscalibration, including cases of approximate bias cancellation. Empirical experiments using stock returns confirm that associational signals can produce viable frontiers under classical mean-variance optimization. These findings reframe the role of causal modeling in quantitative finance: while causality may offer interpretability and robustness in specific contexts, it is directional informativeness, not structural fidelity, that is essential for effective signal-based portfolio design.