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Causal Inference

The Danger of Ignoring Endogeneity in Impact Assessment Studies

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The Danger of Ignoring Endogeneity in Impact Assessment Studies

Impact assessments are widely regarded as the gold standard for evidence-based policymaking. Yet, behind many โ€œcausalโ€ claims lies a silent but serious flaw: endogeneity. Most researchers focus on sample size, statistical fit, or significance levels. However, ignoring endogeneity can transform expensive evaluations into misleading exercises. Instead of guiding progress, such studies risk misallocating resources and … Read more

Inferential Statistics Misused: When Correlation Becomes Causation (Wrongly)

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Inferential Statistics Misused: When Correlation Becomes Causation (Wrongly)

Inferential statistics are powerful tools for uncovering patterns and testing relationships. But they are also easily misused. One of the most frequent and damaging mistakes we see at Research & Report Consulting is treating correlation as causation. This statistical misstep is more than a technical flawโ€”it undermines credibility, damages publication chances, and can lead to … Read more

The Wrong Use of Control Variables is Killing Your Regression Model

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The Wrong Use of Control Variables is Killing Your Regression Model

Regression analysis is one of the most widely used statistical tools in empirical research. Yet, beneath many published models lies a silent killer of validity: the misuse of control variables. Too often, researchers include controls reflexively โ€” without a clear understanding of their role in causal inference or model integrity. This article dives into what … Read more