This problem is known to be complicated by many unobserved confounders.

Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics. Recent work focuses on designing neural network architectures and regularization functions to allow for scalable estimation of average and individual-level dose-response curves from high-dimensional, large-sample data. Such methodologies assume ignorability (observation of all confounding variables) and positivity (observation of all treatment levels for every covariate value describing a set of units), assumptions problematic in the continuous treatment regime. Scalable sensitivity and uncertainty analyses to understand the ignorance induced in causal estimates when these assumptions are relaxed are less studied. Here, we develop a continuous treatment-effect marginal sensitivity model (CMSM) and derive bounds that agree with the observed data and a researcher-defined level of hidden confounding. We introduce a scalable algorithm and uncertainty-aware deep models to derive and estimate these bounds for high-dimensional, large-sample observational data. We work in concert with climate scientists interested in the climatological impacts of human emissions on cloud properties using satellite observations from the past 15 years. This problem is known to be complicated by many unobserved confounders.

Object recognition using image-set or video sequence as input tends to be more robust since image-set or video sequence provides much more information than single snap-shot about the variability in the appearance of the target subject. Constrained Mutual Subspace Method (CMSM) is one of the state-of-the-art algorithms for imageset based object recognition by first projecting the image-set patterns onto the so-called generalized difference subspace then classifying based on the principal angle based mutual subspace distance. By treating the subspace bases for each image-set patterns as basic elements in the grassmann manifold, this paper presents a framework for robust image-set based recognition by CMSM based ensemble learning in a boosting way. The proposed Boosting Constrained Mutual Subspace Method(BCMSM) improves the original CMSM in the following ways: a) The proposed BCMSM algorithm is insensitive to the dimension of the generalized differnce subspace while the performance of the original CMSM algorithm is quite dependent on the dimension and the selecting of optimum choice is quite empirical and case-dependent; b) By taking advantage of both boosting and CMSM techniques, the generalization ability is improved and much higher classification performance can be achieved. Extensive experiments on real-life data sets (two face recognition tasks and one 3D object category classification task) show that the proposed method outperforms the previous state-of-the-art algorithms greatly in terms of classification accuracy.