🧭 The Challenge:

Measuring the causal effect of state behavior on outcomes faces two central obstacles: behavior is endogenous, and unobserved confounders are pervasive. Commonly used matching methods are poorly suited when confounders are unobserved. Heckman-style multiple-equation models address unobserved confounding but depend on rigid functional-form assumptions that can introduce substantial bias in estimates of average treatment effects.

🔎 What Method Is Proposed:

A class of flexible joint likelihood models is introduced to confront both problems simultaneously while avoiding strong functional-form restrictions. These models jointly model selection and outcomes in a likelihood framework but allow for flexible specification that reduces reliance on parametric assumptions.

🧪 How Models Were Tested:

• Neutral simulation experiments compare flexible joint likelihood models against standard alternatives (matching methods and Heckman-style multiple-equation models).
• Performance is evaluated with respect to bias in estimating average treatment effects across a range of data-generating scenarios.

📊 Key Findings:

• Flexible joint likelihood models substantially reduce bias compared to competing models.
• Simulations show bias reductions of roughly 55% up to greater than 90% relative to standard approaches.

📚 Applied Demonstration:

A reanalysis of Simmons (2000) is presented, revisiting the effect of Article VIII commitment on compliance with the IMF’s currency-restriction regime. The flexible joint likelihood approach is used to reassess that classic substantive finding under weaker functional-form assumptions.

💡 Why It Matters:

• Provides a practical alternative for causal inference when state behavior is endogenous and unobserved confounding is likely.
• Preserves identification benefits of multiple-equation approaches while mitigating functional-form bias, improving credibility of average treatment effect estimates in political science applications.