272 research outputs found
Causal Confusion in Imitation Learning
Behavioral cloning reduces policy learning to supervised learning by training
a discriminative model to predict expert actions given observations. Such
discriminative models are non-causal: the training procedure is unaware of the
causal structure of the interaction between the expert and the environment. We
point out that ignoring causality is particularly damaging because of the
distributional shift in imitation learning. In particular, it leads to a
counter-intuitive "causal misidentification" phenomenon: access to more
information can yield worse performance. We investigate how this problem
arises, and propose a solution to combat it through targeted
interventions---either environment interaction or expert queries---to determine
the correct causal model. We show that causal misidentification occurs in
several benchmark control domains as well as realistic driving settings, and
validate our solution against DAgger and other baselines and ablations.Comment: Published at NeurIPS 2019 9 pages, plus references and appendice
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure
We present a very general approach to learning the structure of causal models
based on d-separation constraints, obtained from any given set of overlapping
passive observational or experimental data sets. The procedure allows for both
directed cycles (feedback loops) and the presence of latent variables. Our
approach is based on a logical representation of causal pathways, which permits
the integration of quite general background knowledge, and inference is
performed using a Boolean satisfiability (SAT) solver. The procedure is
complete in that it exhausts the available information on whether any given
edge can be determined to be present or absent, and returns "unknown"
otherwise. Many existing constraint-based causal discovery algorithms can be
seen as special cases, tailored to circumstances in which one or more
restricting assumptions apply. Simulations illustrate the effect of these
assumptions on discovery and how the present algorithm scales.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
Joint Causal Inference from Multiple Contexts
The gold standard for discovering causal relations is by means of
experimentation. Over the last decades, alternative methods have been proposed
that can infer causal relations between variables from certain statistical
patterns in purely observational data. We introduce Joint Causal Inference
(JCI), a novel approach to causal discovery from multiple data sets from
different contexts that elegantly unifies both approaches. JCI is a causal
modeling framework rather than a specific algorithm, and it can be implemented
using any causal discovery algorithm that can take into account certain
background knowledge. JCI can deal with different types of interventions (e.g.,
perfect, imperfect, stochastic, etc.) in a unified fashion, and does not
require knowledge of intervention targets or types in case of interventional
data. We explain how several well-known causal discovery algorithms can be seen
as addressing special cases of the JCI framework, and we also propose novel
implementations that extend existing causal discovery methods for purely
observational data to the JCI setting. We evaluate different JCI
implementations on synthetic data and on flow cytometry protein expression data
and conclude that JCI implementations can considerably outperform
state-of-the-art causal discovery algorithms.Comment: Final version, as published by JML
- …