10 research outputs found
Empirically assessing the plausibility of unconfoundedness in observational studies
The possibility of unmeasured confounding is one of the main limitations for causal inference from observational studies. There are different methods for partially empirically assessing the plausibility of unconfoundedness. However, most currently available methods require (at least partial) assumptions about the confounding structure, which may be difficult to know in practice. In this paper we describe a simple strategy for empirically assessing the plausibility of conditional unconfoundedness (i.e., whether the candidate set of covariates suffices for confounding adjustment) which does not require any assumptions about the confounding structure, requiring instead assumptions related to temporal ordering between covariates, exposure and outcome (which can be guaranteed by design), measurement error and selection into the study. The proposed method essentially relies on testing the association between a subset of covariates (those associated with the exposure given all other covariates) and the outcome conditional on the remaining covariates and the exposure. We describe the assumptions underlying the method, provide proofs, use simulations to corroborate the theory and illustrate the method with an applied example assessing the causal effect of length-for-age measured in childhood and intelligence quotient measured in adulthood using data from the 1982 Pelotas (Brazil) birth cohort. We also discuss the implications of measurement error and some important limitations
Empirically assessing the plausibility of unconfoundedness in observational studies
The possibility of unmeasured confounding is one of the main limitations for
causal inference from observational studies. There are different methods for
partially empirically assessing the plausibility of unconfoundedness. However,
most currently available methods require (at least partial) assumptions about
the confounding structure, which may be difficult to know in practice. In this
paper we describe a simple strategy for empirically assessing the plausibility
of conditional unconfoundedness (i.e., whether the candidate set of covariates
suffices for confounding adjustment) which does not require any assumptions
about the confounding structure, requiring instead assumptions related to
temporal ordering between covariates, exposure and outcome (which can be
guaranteed by design), measurement error and selection into the study. The
proposed method essentially relies on testing the association between a subset
of covariates (those associated with the exposure given all other covariates)
and the outcome conditional on the remaining covariates and the exposure. We
describe the assumptions underlying the method, provide proofs, use simulations
to corroborate the theory and illustrate the method with an applied example
assessing the causal effect of length-for-age measured in childhood and
intelligence quotient measured in adulthood using data from the 1982 Pelotas
(Brazil) birth cohort. We also discuss the implications of measurement error
and some important limitations
Ancestral Causal Inference
Constraint-based causal discovery from limited data is a notoriously
difficult challenge due to the many borderline independence test decisions.
Several approaches to improve the reliability of the predictions by exploiting
redundancy in the independence information have been proposed recently. Though
promising, existing approaches can still be greatly improved in terms of
accuracy and scalability. We present a novel method that reduces the
combinatorial explosion of the search space by using a more coarse-grained
representation of causal information, drastically reducing computation time.
Additionally, we propose a method to score causal predictions based on their
confidence. Crucially, our implementation also allows one to easily combine
observational and interventional data and to incorporate various types of
available background knowledge. We prove soundness and asymptotic consistency
of our method and demonstrate that it can outperform the state-of-the-art on
synthetic data, achieving a speedup of several orders of magnitude. We
illustrate its practical feasibility by applying it on a challenging protein
data set.Comment: In Proceedings of Advances in Neural Information Processing Systems
29 (NIPS 2016
Proof and Uncertainty in Causal Claims
Causal questions drive scientific enquiry. From Hume to Granger, and Rubin to Pearl the history of science is full of examples of scientists testing new theories in an effort to uncover causal mechanisms. The difficulty of drawing causal conclusions from observational data has prompted developments in new methodologies, most notably in the area of graphical models. We explore the relationship between existing theories about causal mechanisms in a social science domain, new mathematical and statistical modelling methods, the role of mathematical proof and the importance of accounting for uncertainty. We show that, while the mathematical sciences rely on their modelling assumptions, dialogue with the social sciences calls for continual extension of these models. We show how changing model assumptions lead to innovative causal structures and more nuanced casual explanations. We review differing techniques for determining cause in different disciplines using causal theories from psychology, medicine, and economics
Causal Inference through a Witness Protection Program
One of the most fundamental problems in causal inference is the estimation of
a causal effect when variables are confounded. This is difficult in an
observational study, because one has no direct evidence that all confounders
have been adjusted for. We introduce a novel approach for estimating causal
effects that exploits observational conditional independencies to suggest
"weak" paths in a unknown causal graph. The widely used faithfulness condition
of Spirtes et al. is relaxed to allow for varying degrees of "path
cancellations" that imply conditional independencies but do not rule out the
existence of confounding causal paths. The outcome is a posterior distribution
over bounds on the average causal effect via a linear programming approach and
Bayesian inference. We claim this approach should be used in regular practice
along with other default tools in observational studies.Comment: 41 pages, 7 figure
Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
In many fields of scientific research and real-world applications, unbiased
estimation of causal effects from non-experimental data is crucial for
understanding the mechanism underlying the data and for decision-making on
effective responses or interventions. A great deal of research has been
conducted to address this challenging problem from different angles. For
estimating causal effect in observational data, assumptions such as Markov
condition, faithfulness and causal sufficiency are always made. Under the
assumptions, full knowledge such as, a set of covariates or an underlying
causal graph, is typically required. A practical challenge is that in many
applications, no such full knowledge or only some partial knowledge is
available. In recent years, research has emerged to use search strategies based
on graphical causal modelling to discover useful knowledge from data for causal
effect estimation, with some mild assumptions, and has shown promise in
tackling the practical challenge. In this survey, we review these data-driven
methods on causal effect estimation for a single treatment with a single
outcome of interest and focus on the challenges faced by data-driven causal
effect estimation. We concisely summarise the basic concepts and theories that
are essential for data-driven causal effect estimation using graphical causal
modelling but are scattered around the literature. We identify and discuss the
challenges faced by data-driven causal effect estimation and characterise the
existing methods by their assumptions and the approaches to tackling the
challenges. We analyse the strengths and limitations of the different types of
methods and present an empirical evaluation to support the discussions. We hope
this review will motivate more researchers to design better data-driven methods
based on graphical causal modelling for the challenging problem of causal
effect estimation.Comment: 35 pages, 10 figures and 2 table, Accepted by ACM Computing Survey