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Evidence-based policies

from Lars Syll

Evidence-based theories and policies are highly valued nowadays. Randomization is supposed to control for bias from unknown confounders. The received opinion is that evidence based on randomized experiments, therefore, is the best.

More and more economists have also lately come to advocate randomization as the principal method for ensuring being able to make valid causal inferences.

rowsonYours truly would, however, rather argue that randomization, just as econometrics, promises more than it can deliver, basically because it requires assumptions that in practice are not possible to maintain. Just as econometrics, randomization is basically a deductive method. Given the assumptions (such as manipulability, transitivity, separability, additivity, linearity, etc.) these methods deliver deductive inferences. The problem, of course, is that we will never completely know when the assumptions are right. And although randomization may contribute to controlling for confounding, it does not guarantee it, since genuine randomness presupposes infinite experimentation and we know all real experimentation is finite. And even if randomization may help to establish average causal effects, it says nothing of individual effects unless homogeneity is added to the list of assumptions. Real target systems are seldom epistemically isomorphic to our axiomatic-deductive models/systems, and even if they were, we still have to argue for the external validity of the conclusions reached from within these epistemically convenient models/systems. Causal evidence generated by randomization procedures may be valid in ‘closed’ models, but what we usually are interested in, is causal evidence in the real target system we happen to live in.

The point of making a randomized experiment is often said to be that it ‘ensures’ that any correlation between a supposed cause and effect indicates a causal relation. This is believed to hold since randomization (allegedly) ensures that a supposed causal variable does not correlate with other variables that may influence the effect.

The problem with that simplistic view on randomization is that the claims made are both exaggerated and false:

• Even if you manage to do the assignment to treatment and control groups ideally random, the sample selection certainly is — except in extremely rare cases — not random. Even if we make a proper randomized assignment, if we apply the results to a biased sample, there is always the risk that the experimental findings will not apply. What works ‘there,’ does not work ‘here.’ Randomization hence does not ‘guarantee ‘ or ‘ensure’ making the right causal claim. Although randomization may help us rule out certain possible causal claims, randomization per se does not guarantee anything!

• Even if both sampling and assignment are made in an ideal random way, performing standard randomized experiments only give you averages. The problem here is that although we may get an estimate of the ‘true’ average causal effect, this may ‘mask’ important heterogeneous effects of a causal nature. Although we get the right answer of the average causal effect being 0, those who are ‘treated’ may have causal effects equal to -100, and those ‘not treated’ may have causal effects equal to 100. Contemplating being treated or not, most people would probably be interested in knowing about this underlying heterogeneity and would not consider the average effect particularly enlightening.

• There is almost always a trade-off between bias and precision. In real-world settings, a little bias often does not overtrump greater precision. And — most importantly — in case we have a population with sizeable heterogeneity, the average treatment effect of the sample may differ substantially from the average treatment effect in the population. If so, the value of any extrapolating inferences made from trial samples to other populations is highly questionable.

• Since most real-world experiments and trials build on performing one single randomization, what would happen if you kept on randomizing forever, does not help you to ‘ensure’ or ‘guarantee’ that you do not make false causal conclusions in the one particular randomized experiment you actually do perform. It is indeed difficult to see why thinking about what you know you will never do, would make you happy about what you actually do.

Randomization is not a panacea. It is not the best method for all questions and circumstances. Proponents of randomization make claims about its ability to deliver causal knowledge that is simply wrong. There are good reasons to be skeptical of the now popular — and ill-informed — view that randomization is the only valid and best method on the market. It is not.

  1. Yoshinori Shiozawa
    September 24, 2020 at 6:34 pm

    Lars, you can criticize evidence-based policies that is in mode now. But, randomization is simply a method of research. He should distinguish its practice and the method.

    What is more important is that it is necessary to distinguish theory and policy. Why has he added “theories” in his top sentence? What he has argued in this article is only his opposition on policy-making or decision-making based on randomization. There is no mention on theories. We cannot say that theories based on evidence are popular and highly valued.

    But, what is far more important is that evidence-based economics is an important topic in this RWER blog. I do not say that it is popular or strongly supported. But, I believe that Michael Joffe (who appears in this blog with the handle name “evidencebasedeconomics” or “evidencebas”) is proposing very important idea that may revolutionize economics (See his web page Dr Michael Joffe, Imperial College, London). Lars has intentionally neglected Joffe’s proposal and tries to classify “evidence-based economics” in the same class as evidence-based policies backed by randomization.

    I know that the word evidence-based economics is ambiguous. Certain number of people are now talking about evidence-based economics but are thinking of very different thing.

    The worst example is the short paper by Edmund S. Phelps (2006) “Evidence-based economics”. He is defending mainstream economics by the name of evidence-based economics. But, at the same time, we have an article written by economist in business that recommend to re-build economics to evidence-based economics: Sean Harkin 2010 Evidence-based economics / Why putting evidence before equations would make economics more like science.

    There is now a tug of war between mainstream and heterodox economics on which wind the term and the concept of evidence-based.economics. Lars is pushing evidence-based economics into the camp of mainstream economics.

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