Causal-structure Driven Augmentations for Text OOD Generalization

The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthcare. In this work, we propose to use counterfactual data augmentation, guided by knowledge of the causal structure of the data, to simulate interventions on spurious features and to learn more robust text classifiers. We show that this strategy is appropriate in prediction problems where the label is spuriously correlated with an attribute. Under the assumptions of such problems, we discuss the favorable sample complexity of counterfactual data augmentation, compared to importance re-weighting. Pragmatically, we match examples using auxiliary data, based on diff-in-diff methodology, and use a large language model (LLM) to represent a conditional probability of text. Through extensive experimentation on learning caregiver-invariant predictors of clinical diagnoses from medical narratives and on semi-synthetic data, we demonstrate that our method for simulating interventions improves out-of-distribution (OOD) accuracy compared to baseline invariant learning algorithms.Comment: Forthcoming in NeurIPS 202

In the Eye of the Beholder: Robust Prediction with Causal User Modeling

Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recommendation systems, media services, and online marketplaces all exhibit a constant influx of new content -- making relevancy a moving target, to which standard predictive models are not robust. In this paper, we propose a learning framework for relevance prediction that is robust to changes in the data distribution. Our key observation is that robustness can be obtained by accounting for how users causally perceive the environment. We model users as boundedly-rational decision makers whose causal beliefs are encoded by a causal graph, and show how minimal information regarding the graph can be used to contend with distributional changes. Experiments in multiple settings demonstrate the effectiveness of our approach.Comment: Accepted to NeurIPS 202

In the Hunt for Therapeutic Targets: Mimicking the Growth, Metastasis, and Stromal Associations of Early-Stage Lung Cancer Using a Novel Orthotopic Animal Model

BackgroundThe existing shortage of animal models that properly mimic the progression of early-stage human lung cancer from a solitary confined tumor to an invasive metastatic disease hinders accurate characterization of key interactions between lung cancer cells and their stroma. We herein describe a novel orthotopic animal model that addresses these concerns and consequently serves as an attractive platform to study tumor–stromal cell interactions under conditions that reflect early-stage lung cancer.MethodsUnlike previous methodologies, we directly injected small numbers of human or murine lung cancer cells into murine's left lung and longitudinally monitored disease progression. Next, we used green fluorescent protein-tagged tumor cells and immuno-fluorescent staining to determine the tumor's microanatomic distribution and to look for tumor-infiltrating immune cells and stromal cells. Finally, we compared chemokine gene expression patterns in the tumor and lung microenvironment.ResultsWe successfully generated a solitary pulmonary nodule surrounded by normal lung parenchyma that grew locally and spread distally over time. Notably, we found that both fibroblasts and leukocytes are recruited to the tumor's margins and that distinct myeloid cell attracting and CCR2-binding chemokines are specifically induced in the tumor microenvironment.ConclusionOur orthotopic lung cancer model closely mimics the pathologic sequence of events that characterizes early-stage human lung cancer propagation. It further introduces new means to monitor tumor–stromal cell interactions and offers unique opportunities to test therapeutic targets under conditions that reflect early-stage lung cancer. We argue that for such purposes our model is superior to lung cancer models that are based either on genetic induction of epithelial transformation or on ectopic transplantation of malignant cells

Hawking Radiation and Unitary evolution

We find a family of exact solutions to the semi-classical equations (including back-reaction) of two-dimensional dilaton gravity, describing infalling null matter that becomes outgoing and returns to infinity without forming a black hole. When a black hole almost forms, the radiation reaching infinity in advance of the original outgoing null matter has the properties of Hawking radiation. The radiation reaching infinity after the null matter consists of a brief burst of negative energy that preserves unitarity and transfers information faster than the theoretical bound for positive energy.Comment: LaTex file + uuencoded ps version including 4 figure

Semi-infinite Throat as the End-state Geometry of two-dimensional Black Hole Evaporation

We study a modified two-dimensional dilaton gravity theory which is exactly solvable in the semiclassical approximation including back-reaction. The vacuum solutions of this modified theory are asymptotically flat static space-times. Infalling matter forms a black hole if its energy is above a certain threshold. The black hole singularity is initially hidden behind a timelike apparent horizon. As the black hole evaporates by emitting Hawking radiation, the singularity meets the shrinking horizon in finite retarded time to become naked. A natural boundary condition exists at the naked singularity such that for general infalling matter-configuration the evaporating black hole geometries can be matched continuously to a unique static end-state geometry. This end-state geometry is asymptotically flat at its right spatial infinity, while its left spatial infinity is a semi-infinite throat extending into the strong coupling region.Comment: Tex + compressed uuencoded ps version with one figure included, 11

Predictability and Semiclassical Approximation at the onset of Black Hole formation

We combine analytical and numerical techniques to study the collapse of conformally coupled massless scalar fields in semiclassical 2D dilaton gravity, with emphasis on solutions just below criticality when a black hole almost forms. We study classical information and quantum correlations. We show explicitly how recovery of information encoded in the classical initial data from the outgoing classical radiation becomes more difficult as criticality is approached. The outgoing quantum radiation consists of a positive-energy flux, which is essentially the standard Hawking radiation, followed by a negative-energy flux which ensures energy conservation and guarantees unitary evolution through strong correlations with the positive-energy Hawking radiation. As one reaches the critical solution there is a breakdown of unitarity. We show that this breakdown of predictability is intimately related to a breakdown of the semiclassical approximation.Comment: 26 pages RevTex + 8 figures in a separate postscript fil