For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates

Area of Science:

• Statistical Methodology
• Causal Inference Research

Background:

• Controversies in statistical analysis and inference, particularly concerning causal modeling and null bias.
• Human factors have broadened technical discussions into philosophical debates regarding statistical methods.

Framework:

• Addresses misconceptions about the necessity and sufficiency of formal methods, such as potential-outcome models, for valid causal inference.
• Reconciles opposing views by clarifying the requirements and capabilities of formal methods in statistical analysis.

Implementation:

• Focuses on the sound use of formal methods, including causal modeling, statistical inference, and sensitivity analysis.
• Highlights the importance of applying these methods correctly to avoid overconfidence and misinterpretations in statistical practices.

Implications:

• Aims to improve the teaching and application of statistical methods for more reliable causal inference.
• Seeks to prevent the misinterpretations that have historically undermined statistical practices and causal reasoning.