Causal inference is crucial in empirical research for distinguishing causal relationships from mere correlations. It addresses the 'Fundamental Problem of Causal Inference' by employing strategies like RCTs, observational studies, and statistical models to infer counterfactual outcomes. These methods are vital in medicine, economics, and social sciences, impacting policy and intervention decisions.
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1
Difference between causal and correlational relationships
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2
Importance of causal inference in policy-making
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3
Statistical methods in causal inference
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4
To deduce what could have occurred differently, researchers compare outcomes between various ______, which can lead to biases and ______.
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5
Most rigorous causal inference method?
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6
Alternative to RCTs when not feasible?
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7
Other causal inference methods besides RCTs and observational studies?
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8
______ models are statistical tools used to deduce causal relationships from ______ data.
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9
Causal models are vital for evaluating the impact of ______ policies, ______ programs, and changes in ______ algorithms.
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10
Define Simpson's Paradox.
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11
Impact of smoking bans on health outcomes.
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12
Long-term economic benefits of education.
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