37 research outputs found
On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization
Causal discovery from observational data is an important but challenging task
in many scientific fields. Recently, NOTEARS [Zheng et al., 2018] formulates
the causal structure learning problem as a continuous optimization problem
using least-square loss with an acyclicity constraint. Though the least-square
loss function is well justified under the standard Gaussian noise assumption,
it is limited if the assumption does not hold. In this work, we theoretically
show that the violation of the Gaussian noise assumption will hinder the causal
direction identification, making the causal orientation fully determined by the
causal strength as well as the variances of noises in the linear case and the
noises of strong non-Gaussianity in the nonlinear case. Consequently, we
propose a more general entropy-based loss that is theoretically consistent with
the likelihood score under any noise distribution. We run extensive empirical
evaluations on both synthetic data and real-world data to validate the
effectiveness of the proposed method and show that our method achieves the best
in Structure Hamming Distance, False Discovery Rate, and True Positive Rate
matrices
Generalization bound for estimating causal effects from observational network data
Estimating causal effects from observational network data is a significant
but challenging problem. Existing works in causal inference for observational
network data lack an analysis of the generalization bound, which can
theoretically provide support for alleviating the complex confounding bias and
practically guide the design of learning objectives in a principled manner. To
fill this gap, we derive a generalization bound for causal effect estimation in
network scenarios by exploiting 1) the reweighting schema based on joint
propensity score and 2) the representation learning schema based on Integral
Probability Metric (IPM). We provide two perspectives on the generalization
bound in terms of reweighting and representation learning, respectively.
Motivated by the analysis of the bound, we propose a weighting regression
method based on the joint propensity score augmented with representation
learning. Extensive experimental studies on two real-world networks with
semi-synthetic data demonstrate the effectiveness of our algorithm
A Survey on Causal Reinforcement Learning
While Reinforcement Learning (RL) achieves tremendous success in sequential
decision-making problems of many domains, it still faces key challenges of data
inefficiency and the lack of interpretability. Interestingly, many researchers
have leveraged insights from the causality literature recently, bringing forth
flourishing works to unify the merits of causality and address well the
challenges from RL. As such, it is of great necessity and significance to
collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL
methods, and investigate the potential functionality from causality toward RL.
In particular, we divide existing CRL approaches into two categories according
to whether their causality-based information is given in advance or not. We
further analyze each category in terms of the formalization of different
models, ranging from the Markov Decision Process (MDP), Partially Observed
Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment
Regime (DTR). Moreover, we summarize the evaluation matrices and open sources
while we discuss emerging applications, along with promising prospects for the
future development of CRL.Comment: 29 pages, 20 figure
Transferable Time-Series Forecasting under Causal Conditional Shift
This paper focuses on the problem of semi-supervised domain adaptation for
time-series forecasting, which is underexplored in literatures, despite being
often encountered in practice. Existing methods on time-series domain
adaptation mainly follow the paradigm designed for the static data, which
cannot handle domain-specific complex conditional dependencies raised by data
offset, time lags, and variant data distributions. In order to address these
challenges, we analyze variational conditional dependencies in time-series data
and find that the causal structures are usually stable among domains, and
further raise the causal conditional shift assumption. Enlightened by this
assumption, we consider the causal generation process for time-series data and
propose an end-to-end model for the semi-supervised domain adaptation problem
on time-series forecasting. Our method can not only discover the Granger-Causal
structures among cross-domain data but also address the cross-domain
time-series forecasting problem with accurate and interpretable predicted
results. We further theoretically analyze the superiority of the proposed
method, where the generalization error on the target domain is bounded by the
empirical risks and by the discrepancy between the causal structures from
different domains. Experimental results on both synthetic and real data
demonstrate the effectiveness of our method for the semi-supervised domain
adaptation method on time-series forecasting
TAG : Type Auxiliary Guiding for Code Comment Generation
Existing leading code comment generation approaches with the
structure-to-sequence framework ignores the type information of the
interpretation of the code, e.g., operator, string, etc. However, introducing
the type information into the existing framework is non-trivial due to the
hierarchical dependence among the type information. In order to address the
issues above, we propose a Type Auxiliary Guiding encoder-decoder framework for
the code comment generation task which considers the source code as an N-ary
tree with type information associated with each node. Specifically, our
framework is featured with a Type-associated Encoder and a Type-restricted
Decoder which enables adaptive summarization of the source code. We further
propose a hierarchical reinforcement learning method to resolve the training
difficulties of our proposed framework. Extensive evaluations demonstrate the
state-of-the-art performance of our framework with both the auto-evaluated
metrics and case studies.Comment: ACL 2020, Accepte