54 research outputs found
Structure Learning with Continuous Optimization: A Sober Look and Beyond
This paper investigates in which cases continuous optimization for directed
acyclic graph (DAG) structure learning can and cannot perform well and why this
happens, and suggests possible directions to make the search procedure more
reliable. Reisach et al. (2021) suggested that the remarkable performance of
several continuous structure learning approaches is primarily driven by a high
agreement between the order of increasing marginal variances and the
topological order, and demonstrated that these approaches do not perform well
after data standardization. We analyze this phenomenon for continuous
approaches assuming equal and non-equal noise variances, and show that the
statement may not hold in either case by providing counterexamples,
justifications, and possible alternative explanations. We further demonstrate
that nonconvexity may be a main concern especially for the non-equal noise
variances formulation, while recent advances in continuous structure learning
fail to achieve improvement in this case. Our findings suggest that future
works should take into account the non-equal noise variances formulation to
handle more general settings and for a more comprehensive empirical evaluation.
Lastly, we provide insights into other aspects of the search procedure,
including thresholding and sparsity, and show that they play an important role
in the final solutions
Generator Identification for Linear SDEs with Additive and Multiplicative Noise
In this paper, we present conditions for identifying the generator of a
linear stochastic differential equation (SDE) from the distribution of its
solution process with a given fixed initial state. These identifiability
conditions are crucial in causal inference using linear SDEs as they enable the
identification of the post-intervention distributions from its observational
distribution. Specifically, we derive a sufficient and necessary condition for
identifying the generator of linear SDEs with additive noise, as well as a
sufficient condition for identifying the generator of linear SDEs with
multiplicative noise. We show that the conditions derived for both types of
SDEs are generic. Moreover, we offer geometric interpretations of the derived
identifiability conditions to enhance their understanding. To validate our
theoretical results, we perform a series of simulations, which support and
substantiate the established findings
Estimation of the flux at 1450MHz of OB stars for FAST and SKA
Radio observation is crucial to understanding the wind mechanism of OB stars
but very scarce. This work estimates the flux at 1450MHz () of
about 5,000 OB stars identified by the LAMOST spectroscopic survey and
confirmed by the Gaia astrometric as well as astrophysical measurements. The
calculation is performed under the free-free emission mechanism for wind with
the mass loss rate derived from stellar parameters. The estimated distributes from Jy to Jy with the peak at about
Jy. This implies that the complete SKA-II can detect more than half of
them, and some tens of objects are detectable by FAST without considering
source confusion. An array of FAST would increase the detectable sample by two
orders of magnitude.Comment: 15 pages. 8 figure
Advancing Counterfactual Inference through Quantile Regression
The capacity to address counterfactual "what if" inquiries is crucial for
understanding and making use of causal influences. Traditional counterfactual
inference usually assumes a structural causal model is available. However, in
practice, such a causal model is often unknown and may not be identifiable.
This paper aims to perform reliable counterfactual inference based on the
(learned) qualitative causal structure and observational data, without a given
causal model or even directly estimating conditional distributions. We re-cast
counterfactual reasoning as an extended quantile regression problem using
neural networks. The approach is statistically more efficient than existing
ones, and further makes it possible to develop the generalization ability of
the estimated counterfactual outcome to unseen data and provide an upper bound
on the generalization error. Experiment results on multiple datasets strongly
support our theoretical claims
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