1,130 research outputs found
Counterfactual (Non-)identifiability of Learned Structural Causal Models
Recent advances in probabilistic generative modeling have motivated learning
Structural Causal Models (SCM) from observational datasets using deep
conditional generative models, also known as Deep Structural Causal Models
(DSCM). If successful, DSCMs can be utilized for causal estimation tasks, e.g.,
for answering counterfactual queries. In this work, we warn practitioners about
non-identifiability of counterfactual inference from observational data, even
in the absence of unobserved confounding and assuming known causal structure.
We prove counterfactual identifiability of monotonic generation mechanisms with
single dimensional exogenous variables. For general generation mechanisms with
multi-dimensional exogenous variables, we provide an impossibility result for
counterfactual identifiability, motivating the need for parametric assumptions.
As a practical approach, we propose a method for estimating worst-case errors
of learned DSCMs' counterfactual predictions. The size of this error can be an
essential metric for deciding whether or not DSCMs are a viable approach for
counterfactual inference in a specific problem setting. In evaluation, our
method confirms negligible counterfactual errors for an identifiable SCM from
prior work, and also provides informative error bounds on counterfactual errors
for a non-identifiable synthetic SCM
Deep Causal Learning for Robotic Intelligence
This invited review discusses causal learning in the context of robotic
intelligence. The paper introduced the psychological findings on causal
learning in human cognition, then it introduced the traditional statistical
solutions on causal discovery and causal inference. The paper reviewed recent
deep causal learning algorithms with a focus on their architectures and the
benefits of using deep nets and discussed the gap between deep causal learning
and the needs of robotic intelligence
High fidelity image counterfactuals with probabilistic causal models
We present a general causal generative modelling framework for accurate estimation of high fidelity image counterfactuals with deep structural causal models. Estimation of interventional and counterfactual queries for high-dimensional structured variables, such as images, remains a challenging task. We leverage ideas from causal mediation analysis and advances in generative modelling to design new deep causal mechanisms for structured variables in causal models. Our experiments demonstrate that our proposed mechanisms are capable of accurate abduction and estimation of direct, indirect and total effects as measured by axiomatic soundness of counterfactuals
Normalizing Flows for Interventional Density Estimation
Existing machine learning methods for causal inference usually estimate
quantities expressed via the mean of potential outcomes (e.g., average
treatment effect). However, such quantities do not capture the full information
about the distribution of potential outcomes. In this work, we estimate the
density of potential outcomes after Interventional Normalizing Flows.
Specifically, we combine two normalizing flows, namely (i) a teacher flow for
estimating nuisance parameters and (ii) a student flow for a parametric
estimation of the density of potential outcomes. We further develop a tractable
optimization objective via a one-step bias correction for an efficient and
doubly robust estimation of the student flow parameters. As a result our
Interventional Normalizing Flows offer a properly normalized density estimator.
Across various experiments, we demonstrate that our Interventional Normalizing
Flows are expressive and highly effective, and scale well with both sample size
and high-dimensional confounding. To the best of our knowledge, our
Interventional Normalizing Flows are the first fully-parametric, deep learning
method for density estimation of potential outcomes
On the Tractability of Neural Causal Inference
Roth (1996) proved that any form of marginal inference with probabilistic
graphical models (e.g. Bayesian Networks) will at least be NP-hard. Introduced
and extensively investigated in the past decade, the neural probabilistic
circuits known as sum-product network (SPN) offers linear time complexity. On
another note, research around neural causal models (NCM) recently gained
traction, demanding a tighter integration of causality for machine learning. To
this end, we present a theoretical investigation of if, when, how and under
what cost tractability occurs for different NCM. We prove that SPN-based causal
inference is generally tractable, opposed to standard MLP-based NCM. We further
introduce a new tractable NCM-class that is efficient in inference and fully
expressive in terms of Pearl's Causal Hierarchy. Our comparative empirical
illustration on simulations and standard benchmarks validates our theoretical
proofs.Comment: Main paper: 8 pages, References: 2 pages, Appendix: 5 pages. Figures:
5 main, 2 appendi
- ā¦