#SERweek2020 @casualinfer podcast being recorded LIVE NOW! @LucyStats @EpiEllie @AmJEpi
Great discussion about propensity score trimming and how we need to carefully interpret results. Trimming can change who is in the population.
When thinking about #MachineLearning and #CausalInference need to consider what you will be training it against
In the news topic: Table 2 Fallacy – occurs when effect estimates of confounders are shown in the same way as the primary exposure. But they cannot be interpreted in the same way.
Example…media reported smoking as protective for COVID based on a Table 2 like this. But…if we were interested in the causal effect of smoking then this model would be inappropriate as it included many conditions that are potential mediators-and not confounders for this assoc
Important discussion regarding peer review in light of recently retracted articles and considering how to correct errors and also allow forums for intellectual disagreement. Listen to the recording for all of the details.
That's a wrap!! Thanks @EpiEllie @LucyStats!!
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