This approach is not only biased, but compromises the validity of statistical tests and confidence intervals. ![]() I mean, it helps us to play around with the data, until we get what we consciously or unconsciously deem “the right model”. ![]() This is a great improvement to the dominant approach of including and variables in a model based on statistical criteria or, which in my opinion is unavoidable, based on our conscious or unconscious biases about how our findings should look. In addition, DAGs help you identify the minimum set of variable you need to condition on to identify a causal effect. This not only adds to the quality of you study, but could cost-saving. In particular, they help you figure out what variables you need to measure and when, so that a causal effect could be identified. This refers to whether the stable-unit-treatment-value assumption (SUTVA) is met. DAGs help you define which causal questions can be answered with de data at hand. Pavlos’ makes some key points regarding the use of DAGs.
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