The search vessel Seabed Constructor has just arrived in the new search area, outfitted with a team of eight autonomous underwater vehicles AUVs. Included in that area are three locations that CSIRO has identified as high priority, as determined from satellite images of floating objects and complex drift models.
My overall reading is this: Many of the claims of the paper are weakened. The link to health outcomes especially anemia is weakened. The evidence of externalities is weakened. The evidence for externalities on the untreated schools specifically was never clearly there. What remains is a tightly estimated coefficient on the direct effect linking being in a treated school and attending classes.
However there are still at least three reasons to worry about this main effect: In the second year it is much smaller and not significant. All three of these claims are consistent with the possibility that this attendance effect, if real, is not an effect of the medication itself.
Also remaining is evidence that worm medication gets rid of worms, but no one ever seemed to doubt that. Five takes on the analysis There are different things to look out for in every paper, but I think five key things that very often warrant particular attention are the following: On what basis does the paper claim to have estimated a true causal effect?
For an RCT the identification is generally justified by appeal to the virtues of random assignment to treatment. How do you measure the inputs and outputs?
Are the measures reliable? How do you map from the quantities that you estimate to the quantities you care about? For example from a coefficient in a regression table to a claim about effects in some population. Though not essential for causal inference, compelling accounts tend to have plausible stories attached to them so that we can be convinced of the logic and we can reason about where else we might expect such effects to operate.
How were the results communicated? Did the right things get highlighted? In my reading of the replication and reanalysis, and re-reading of the paper, there are possibly significant concerns regarding all five dimensions.
So many correlations suggest spurious causal relations. Did taking my umbrella out cause it to rain? More subtly, did wearing rainboots cause me to take my umbrella out? Social scientists have come up with a few strategies to figure out causal relations with confidence, but the most important is the use of randomization.
Miguel and Kremer and their colleagues have had a huge impact on development economics and beyond by advancing the use of randomization to assess causal effects. The basic insight of randomization is a simple one: First that there really is randomization.
Third, that the allocation of the treatment works only through the treatment itself and not through how it is allocated technically this is an exclusion restriction.
How well does it do on these three criteria? Thus to claim the benefits of randomization you need to make an as-if random assumption. In this case, given the method that was used, I expect many people would be willing to make that assumption. As I pointed out beforeregression with interactions does not restore as-if randomization.
An implication is that the claim of unbiased estimates of the spillover effect depends here on the model and not just the design. For instance if I give a medication randomly to one of two sick people and they both get better because of it, then the medication will have helped two people, but in comparing treatment and control I will think it helped none.
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MK are keenly conscious of this; they criticize others for not taking it into account, and they argue that many of their own estimates are underestimates because of this. They may be right but if you are depending on a model for inference you can be off in very surprising ways.
In an example I provided before you can see how linear regression can pull a positive effect from a zero when it does not take account of assignment propensities properly even with interactions included.
The bottom line is that whereas in the absence of spillovers, randomization can get you an unbiased estimate of the average treatment effect, in the presence of spillovers, the randomization can only justify claims to unbiasedness conditional on the particular model of spillovers being correct and correctly estimated.
I think this one is bothering the epidemiologists a lot. When health researchers administer treatments they are very worried about various kinds of spurious effects: They use placebos and they blind patients to treatment status because they want to make sure that the only thing that is being done differently by the assignment to treatment is the provision of the treatment itself.
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