• Corpus ID: 219179893

The role of exchangeability in causal inference

@article{Saarela2020TheRO,
  title={The role of exchangeability in causal inference},
  author={Olli Saarela and David A. Stephens and Erica E. M. Moodie},
  journal={arXiv: Methodology},
  year={2020}
}
The notion of exchangeability has been recognized in the causal inference literature in various guises, but only rarely in the original Bayesian meaning as a symmetry property between individual units in statistical inference. Since the latter is a standard ingredient in Bayesian inference, we argue that in Bayesian causal inference it is natural to link the causal model, including the notion of confounding and definition of causal contrasts of interest, to the concept of exchangeability. Here… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 70 REFERENCES
Bayesian Theory
  • Wiley, Chichester.
  • 1994
The Role of Exchangeability in Inference
Abstract : This paper is concerned with basic problems of statistical inference. The thesis is in three parts: (1) that inference is a procedure whereby one passes from a population (or sample) to a
Causal Inference from Observational Data: A Bayesian Predictive Approach
Causality is a challenging topic for anyone to consider in a formal way. Already the concept itself is problematic, and often people have sharply different opinions of its foundations. However,
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview
TLDR
It is shown that formulating the problem of assessing dynamic treatment strategies as a problem of decision analysis brings clarity, simplicity and generality.
Bayesian Data Analysis, Second Edition
  • Chapman & Hall/CRC, Boca Raton, FL.
  • 2004
Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs
TLDR
This work shows that when one's goal is to test for or estimate an effect of a sequentially applied treatment, this natural parameterization has serious deficiencies, and by reparameterizing the graph using structural nested models, these deficiencies can be avoided.
Concerning the consistency assumption in causal inference.
TLDR
A refinement of the consistency assumption is proposed that makes clear that the consistency statement is in fact an assumption and not an axiom or definition, and sheds light on the distinction between intervention and choice in reasoning about causality.
Identifiability, exchangeability, and epidemiological confounding revisited
  • Epidemiologic Perspectives & Innovations 6. doi:10.1186/1742-5573-6-4.
  • 2009
Marginal Structural Models for the Estimation of Direct and Indirect Effects
TLDR
The estimation of controlled direct effects can be carried out by fitting a marginal structural model and using inverse probability of treatment weighting and the marginal structural models used to estimate natural direct and indirect effects are made conditional on the covariates.
The consistency statement in causal inference: a definition or an assumption?
Three assumptions sufficient to identify the average causal effect are consistency, positivity, and exchangeability (ie, “no unmeasured confounders and no informative censoring,” or “ignorability of
...
1
2
3
4
5
...