Bias and Causation: Models and Judgment for Valid Comparisons
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A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects
Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats...



