3 research outputs found
A General Algorithm for Deciding Transportability of Experimental Results
Generalizing empirical findings to new environments, settings, or populations
is essential in most scientific explorations. This article treats a particular
problem of generalizability, called "transportability", defined as a license to
transfer information learned in experimental studies to a different population,
on which only observational studies can be conducted. Given a set of
assumptions concerning commonalities and differences between the two
populations, Pearl and Bareinboim (2011) derived sufficient conditions that
permit such transfer to take place. This article summarizes their findings and
supplements them with an effective procedure for deciding when and how
transportability is feasible. It establishes a necessary and sufficient
condition for deciding when causal effects in the target population are
estimable from both the statistical information available and the causal
information transferred from the experiments. The article further provides a
complete algorithm for computing the transport formula, that is, a way of
combining observational and experimental information to synthesize bias-free
estimate of the desired causal relation. Finally, the article examines the
differences between transportability and other variants of generalizability
Causal Inference and Data-Fusion in Econometrics
Learning about cause and effect is arguably the main goal in applied
econometrics. In practice, the validity of these causal inferences is
contingent on a number of critical assumptions regarding the type of data that
has been collected and the substantive knowledge that is available. For
instance, unobserved confounding factors threaten the internal validity of
estimates, data availability is often limited to non-random, selection-biased
samples, causal effects need to be learned from surrogate experiments with
imperfect compliance, and causal knowledge has to be extrapolated across
structurally heterogeneous populations. A powerful causal inference framework
is required to tackle these challenges, which plague most data analysis to
varying degrees. Building on the structural approach to causality introduced by
Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the
artificial intelligence (AI) literature has developed a wide array of
techniques for causal learning that allow to leverage information from various
imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016).
In this paper, we discuss recent advances in this literature that have the
potential to contribute to econometric methodology along three dimensions.
First, they provide a unified and comprehensive framework for causal inference,
in which the aforementioned problems can be addressed in full generality.
Second, due to their origin in AI, they come together with sound, efficient,
and complete algorithmic criteria for automatization of the corresponding
identification task. And third, because of the nonparametric description of
structural models that graph-theoretic approaches build on, they combine the
strengths of both structural econometrics as well as the potential outcomes
framework, and thus offer a perfect middle ground between these two competing
literature streams.Comment: Abstract change
Causal Transportability with Limited Experiments
We address the problem of transferring causal knowledge learned in one environment to another, potentially different environment, when only limited experiments may be conducted at the source. This generalizes the treatment of transportability introduced in [Pearl and Bareinboim, 2011; Bareinboim and Pearl, 2012b], which deals with transferring causal information when any experiment can be conducted at the source. Given that it is not always feasible to conduct certain controlled experiments, we consider the decision problem whether experiments on a selected subset Z of variables together with qualitative assumptions encoded in a diagram may render causal effects in the target environment computable from the available data. This problem, which we call z-transportability, reduces to ordinary transportability when Z is all-inclusive, and, like the latter, can be given syntactic characterization using the do-calculus [Pearl, 1995; 2000]. This paper establishes a necessary and sufficient condition for causal effects in the target domain to be estimable from both the non-experimental information available and the limited experimental information transferred from the source. We further provides a complete algorithm for computing the transport formula, that is, a way of fusing experimental and observational information to synthesize an unbiased estimate of the desired causal relation