18 research outputs found
Confounding Equivalence in Causal Inference
The paper provides a simple test for deciding, from a given causal diagram,
whether two sets of variables have the same bias-reducing potential under
adjustment. The test requires that one of the following two conditions holds:
either (1) both sets are admissible (i.e., satisfy the back-door criterion) or
(2) the Markov boundaries surrounding the manipulated variable(s) are identical
in both sets. Applications to covariate selection and model testing are
discussed.Comment: Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010
Causally Invariant Predictor with Shift-Robustness
This paper proposes an invariant causal predictor that is robust to
distribution shift across domains and maximally reserves the transferable
invariant information. Based on a disentangled causal factorization, we
formulate the distribution shift as soft interventions in the system, which
covers a wide range of cases for distribution shift as we do not make prior
specifications on the causal structure or the intervened variables. Instead of
imposing regularizations to constrain the invariance of the predictor, we
propose to predict by the intervened conditional expectation based on the
do-operator and then prove that it is invariant across domains. More
importantly, we prove that the proposed predictor is the robust predictor that
minimizes the worst-case quadratic loss among the distributions of all domains.
For empirical learning, we propose an intuitive and flexible estimating method
based on data regeneration and present a local causal discovery procedure to
guide the regeneration step. The key idea is to regenerate data such that the
regenerated distribution is compatible with the intervened graph, which allows
us to incorporate standard supervised learning methods with the regenerated
data. Experimental results on both synthetic and real data demonstrate the
efficacy of our predictor in improving the predictive accuracy and robustness
across domains
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
We prove the main rules of causal calculus (also called do-calculus) for i/o
structural causal models (ioSCMs), a generalization of a recently proposed
general class of non-/linear structural causal models that allow for cycles,
latent confounders and arbitrary probability distributions. We also generalize
adjustment criteria and formulas from the acyclic setting to the general one
(i.e. ioSCMs). Such criteria then allow to estimate (conditional) causal
effects from observational data that was (partially) gathered under selection
bias and cycles. This generalizes the backdoor criterion, the
selection-backdoor criterion and extensions of these to arbitrary ioSCMs.
Together, our results thus enable causal reasoning in the presence of cycles,
latent confounders and selection bias. Finally, we extend the ID algorithm for
the identification of causal effects to ioSCMs.Comment: Accepted for publication in Conference on Uncertainty in Artificial
Intelligence 2019 (UAI-2019
Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs
We present a graphical criterion for covariate adjustment that is sound and
complete for four different classes of causal graphical models: directed
acyclic graphs (DAGs), maximum ancestral graphs (MAGs), completed partially
directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our
criterion unifies covariate adjustment for a large set of graph classes.
Moreover, we define an explicit set that satisfies our criterion, if there is
any set that satisfies our criterion. We also give efficient algorithms for
constructing all sets that fulfill our criterion, implemented in the R package
dagitty. Finally, we discuss the relationship between our criterion and other
criteria for adjustment, and we provide new soundness and completeness proofs
for the adjustment criterion for DAGs.Comment: 58 pages, 12 figures, to appear in JML
Frameworks for Estimating Causal Effects in Observational Settings: Comparing Confounder Adjustment and Instrumental Variables
To estimate causal effects, analysts performing observational studies in
health settings utilize several strategies to mitigate bias due to confounding
by indication. There are two broad classes of approaches for these purposes:
use of confounders and instrumental variables (IVs). Because such approaches
are largely characterized by untestable assumptions, analysts must operate
under an indefinite paradigm that these methods will work imperfectly. In this
tutorial, we formalize a set of general principles and heuristics for
estimating causal effects in the two approaches when the assumptions are
potentially violated. This crucially requires reframing the process of
observational studies as hypothesizing potential scenarios where the estimates
from one approach are less inconsistent than the other. While most of our
discussion of methodology centers around the linear setting, we touch upon
complexities in non-linear settings and flexible procedures such as target
minimum loss-based estimation (TMLE) and double machine learning (DML). To
demonstrate the application of our principles, we investigate the use of
donepezil off-label for mild cognitive impairment (MCI). We compare and
contrast results from confounder and IV methods, traditional and flexible,
within our analysis and to a similar observational study and clinical trial