Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods for causal sensitivity analysis are per-instance procedures, meaning that changes to the dataset, causal query, sensitivity level, or treatment require new computation. Here, we instead present an in-context learning approach. Specifically, we propose an amortized approach to causal sensitivity analysis based on prior-data fitted networks. A key challenge is that the sensitivity bounds are not directly available when sampling training data. To address this, we develop a general prior-data construction that is applicable across the class of generalized treatment sensitivity models. Our construction involves a Lagrangian scalarization of the objective to generate training labels for the bounds through a tradeoff between causal effect min/max-imization and sensitivity model violation, which avoids model-specific analytical derivations. We further show that, under standard convexity and linearity conditions, our objective recovers the full Pareto frontier of solutions. Empirically, we demonstrate our amortized approach across various datasets, causal queries, and sensitivity levels, where our approach achieves a test-time computation that is orders of magnitude faster than per-instance methods. To the best of our knowledge, ours is the first foundation model for in-context learning for causal sensitivity analysis.

Constraint-based causal discovery is widely used for learning causal structures, but heavy reliance on conditional independence (CI) testing makes it computationally expensive in high-dimensional settings. To mitigate this limitation, many divide-and-conquer frameworks have been proposed, but most assume causal sufficiency, i.e., no latent variables. In this paper, we show that divide-and-conquer strategies can be theoretically generalized beyond causal sufficiency to settings with latent variables. Specifically, we propose a recursive decomposition framework, termed DiCoLa, that enables divide-and-conquer causal discovery in the presence of latent variables. It recursively decomposes the global learning task into smaller subproblems and integrates their solutions through a principled reconstruction step to recover the global structure. We theoretically establish the soundness and completeness of the proposed framework. Extensive experiments on synthetic data demonstrate that our approach significantly improves computational efficiency across a range of causal discovery algorithms, while experiments on a real-world dataset further illustrate its practical effectiveness.

We study sparse recovery when observations come from mixed-quality sources: a small collection of high-quality measurements with small noise variance and a larger collection of lower-quality measurements with higher variance. For this heterogeneous-noise setting, we establish sample-size conditions for information-theoretic and algorithmic recovery. On the information-theoretic side, we show that it is sufficient for $(n_1, n_2)$ to satisfy a linear trade-off defining the Price of Quality: the number of low-quality samples needed to replace one high-quality sample. In the agnostic setting, where the decoder is completely agnostic to the quality of the data, it is uniformly bounded, and in particular one high-quality sample is never worth more than two low-quality samples for this sufficient condition to hold. In the informed setting, where the decoder is informed of per-sample variances, the price of quality can grow arbitrarily large. On the algorithmic side, we analyze the LASSO in the agnostic setting and show that the recovery threshold matches the homogeneous-noise case and only depends on the average noise level, revealing a striking robustness of computational recovery to data heterogeneity. Together, these results give the first conditions for sparse recovery with mixed-quality data and expose a fundamental difference between how the information-theoretic and algorithmic thresholds adapt to changes in data quality.

Ilya Sutskever Stands by His Role in Sam Altman's OpenAI Ouster: 'I Didn't Want It to Be Destroyed' The former OpenAI chief scientist may be estranged from the company, but he still came to its defense as he testified on Monday. Elon Musk's trial against OpenAI and Microsoft entered its final stretch on Monday, with testimony from Microsoft CEO Satya Nadella, former OpenAI chief scientist Ilya Sutskever, and current OpenAI chairman Bret Taylor. Sutskever drew the spotlight, revealing an ownership stake in OpenAI's $850-billion for-profit arm that is currently worth about $7 billion. That makes him one of the largest known individual shareholders of OpenAI. Earlier in the trial, OpenAI president Greg Brockman acknowledged for the first time that he has around $30 billion worth of OpenAI shares .

Security around Beijing's historic Tiananmen Square has been heightened for days, with rumours on social media swirling of a special parade or some big, choreographed event. Preparations for this major event have started with a whisper, but China appears ready to put on a show for US President Donald Trump. The visit will include talks, a banquet, and a visit to the Temple of Heaven, a complex of imperial temples where emperors would pray for a good harvest. And both Trump and Chinese President Xi Jinping will be hoping the visit will bear fruit. This summit between the world's two most powerful leaders is set to be one of the most consequential encounters for years.

Yes, it works--if you have small fingers. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Fitting everything inside resembles a game of'Tetris.' Breakthroughs, discoveries, and DIY tips sent six days a week. Leftover Altoid tins are staple components in all types of handy, DIY projects . Once you eat the mints, the aluminum containers routinely house basic first aid kits, miniature speakers, sewing accessories, and even watercolor paints.

Testing for'Bad Cholesterol' Doesn't Tell the Whole Story So why don't more doctors use it? For decades, assessing cholesterol risk has been built around a simple idea: Lower "bad" cholesterol, lower your chance of a heart attack . The test at the center of that approach measures how much low-density lipoprotein, or LDL cholesterol, is circulating in part of the blood. It has shaped everything from clinical guidelines to the widespread use of statins, medications that reduce LDL. Lowering LDL cholesterol reduces heart attacks, strokes, and early death.

We can now add cybercrimes to the list of growing concerns associated with artificial intelligence. Google's Threat Intelligence Group (GTIG) said it discovered, for the first time ever, a threat actor using a zero-day exploit that it believes was developed by AI. Zero-day vulnerabilities are often the most dangerous since they're unknown to the targets, leaving them with zero days to prepare for the attack. Google said in the report the threat actor was planning to use it in a mass exploitation event, but its proactive discovery may have prevented its use. Google added that it doesn't believe its own Gemini models were used, but still has high confidence an AI model was part of discovering the vulnerability and weaponizing an exploit.

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The complaints, obtained by WIRED, described Bad Bunny's performance as being overly sexual and protested that the show was in Spanish. Bad Bunny performs during halftime of Super Bowl LX at Levi's Stadium in Santa Clara, California. Even before Bad Bunny took to the field, his Super Bowl halftime performance drew controversy, especially from MAGA influencers upset over the Puerto Rican star's comments against Immigration and Customs Enforcement and the fact that he sings in Spanish. Following the performance, which was watched by more than 128 million people, those complaints continued--but they were largely focused on perceived vulgarity in the artist's performance. Following a Freedom of Information Act (FOIA) request from WIRED, the Federal Communications Commission, which regulates communications including broadcast, released 2,155 complaints the agency received about the Super Bowl, most of which were about the halftime show.