The paper "ON MAKING CAUSAL CLAIMS: A REVIEW AND RECOMMENDATIONS" by John Antonakis, Samuel Bendahan, Philippe Jacquart, and Rafael Lalive, published in the *Leadership Quarterly* in 2010, addresses the critical issue of how social scientists can make valid causal claims from correlational data. The authors discuss the conditions under which model estimates can be interpreted causally, emphasizing the importance of endogeneity issues such as omitted variables, selection bias, simultaneity, common methods bias, and measurement error. They present methods to test causal claims in non-experimental settings, including fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. The paper also reviews the methodological rigor of causal claims in leadership research, finding that researchers often fail to address key design and estimation conditions, leading to invalid causal interpretations. The authors conclude with 10 suggestions for improving non-experimental research, emphasizing the need for rigorous methodological practices to ensure the validity of causal claims.The paper "ON MAKING CAUSAL CLAIMS: A REVIEW AND RECOMMENDATIONS" by John Antonakis, Samuel Bendahan, Philippe Jacquart, and Rafael Lalive, published in the *Leadership Quarterly* in 2010, addresses the critical issue of how social scientists can make valid causal claims from correlational data. The authors discuss the conditions under which model estimates can be interpreted causally, emphasizing the importance of endogeneity issues such as omitted variables, selection bias, simultaneity, common methods bias, and measurement error. They present methods to test causal claims in non-experimental settings, including fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. The paper also reviews the methodological rigor of causal claims in leadership research, finding that researchers often fail to address key design and estimation conditions, leading to invalid causal interpretations. The authors conclude with 10 suggestions for improving non-experimental research, emphasizing the need for rigorous methodological practices to ensure the validity of causal claims.