Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Akbari, Sina"'
Artificial Neural Networks (ANNs), including fully-connected networks and transformers, are highly flexible and powerful function approximators, widely applied in fields like computer vision and natural language processing. However, their inability t
Externí odkaz:
http://arxiv.org/abs/2410.14485
Identifying causal effects is a key problem of interest across many disciplines. The two long-standing approaches to estimate causal effects are observational and experimental (randomized) studies. Observational studies can suffer from unmeasured con
Externí odkaz:
http://arxiv.org/abs/2407.05330
Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains. Causal discovery is hampered by two main challenges: li
Externí odkaz:
http://arxiv.org/abs/2403.09300
Autor:
Akbari, Sina, Kiyavash, Negar
The renowned difference-in-differences (DiD) estimator relies on the assumption of 'parallel trends,' which does not hold in many practical applications. To address this issue, the econometrics literature has turned to the triple difference estimator
Externí odkaz:
http://arxiv.org/abs/2402.12583
Drawbacks of ignoring the causal mechanisms when performing imitation learning have recently been acknowledged. Several approaches both to assess the feasibility of imitation and to circumvent causal confounding and causal misspecifications have been
Externí odkaz:
http://arxiv.org/abs/2306.00585
We study the problem of causal structure learning from data using optimal transport (OT). Specifically, we first provide a constraint-based method which builds upon lower-triangular monotone parametric transport maps to design conditional independenc
Externí odkaz:
http://arxiv.org/abs/2305.18210
Autor:
Akbari, Sina, Jamshidi, Fateme, Mokhtarian, Ehsan, Vowels, Matthew J., Etesami, Jalal, Kiyavash, Negar
Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these algorithms hinges on the restrictive assumption of having access to a
Externí odkaz:
http://arxiv.org/abs/2208.04627
Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to lea
Externí odkaz:
http://arxiv.org/abs/2205.02232
Parameter estimation in empirical fields is usually undertaken using parametric models, and such models readily facilitate statistical inference. Unfortunately, they are unlikely to be sufficiently flexible to be able to adequately model real-world p
Externí odkaz:
http://arxiv.org/abs/2202.09096
We study the problem of learning a Bayesian network (BN) of a set of variables when structural side information about the system is available. It is well known that learning the structure of a general BN is both computationally and statistically chal
Externí odkaz:
http://arxiv.org/abs/2112.10884