Why did Trump fire Pam Bondi? Trump's US Fed nominee Warsh vows independence, says he's no'sock puppet' Kevin Warsh, United States President Donald Trump's pick to lead the Federal Reserve, has addressed concerns about his independence pending his appointment to the bank amid fears that Trump could sway his decisions on monetary policy. On Tuesday, Warsh -- who served on the central bank's Board of Governors from 2006 to 2011 -- faced waves of criticism during a confirmation hearing of the Senate Banking Committee where Democrats voiced concerns about the Fed's independence should he be appointed to lead the organisation. "I do not believe the operational independence of monetary policy is particularly threatened when elected officials -- presidents, senators, or members of the House -- state their views on interest rates," Warsh said. "Monetary policy independence is essential. Monetary policymakers must act in the nation's interest . . . Warsh, 56, also called for "regime change" at the US central bank, including a new approach for controlling inflation and a communications overhaul that may discourage his colleagues from saying too much about the direction of monetary policy. Warsh blamed the central bank for an inflation surge after it slashed interest rates to nearly zero in the wake of the COVID-19 pandemic, a move that continues to hurt US households. Concerned by the implications of artificial intelligence for jobs - expected to increase productivity - and prices, he said he would move quickly to see if new data tools could provide better insight on inflation, and would also discourage policymakers from saying too much about where interest rates might be heading. "What the Fed needs are reforms to its frameworks and reforms to its communications," the former Fed governor said. "Too many Fed officials opine about where interest rates should be That is quite unhelpful." Warsh has also long been an advocate for shrinking the Fed's $6.7 trillion balance sheet. In the Tuesday hearing, he said any such plans would take time and must be publicly discussed well in advance. Jai Kedia, a research fellow at the Center for Monetary and Financial Alternatives at the libertarian Cato Institute, told Al Jazeera that there were many "encouraging" signs in Warsh's candidacy. "Warsh is presenting himself as a regime change candidate at a time when the Fed needs serious reform," Kedia noted. "Particularly encouraging was his understanding of the negative effects of QE and his focus on reducing the balance sheet.

Recently, Forré (arXiv:2104.11547, 2021) introduced transitional conditional independence, a notion of conditional independence that provides a unified framework for both random and non-stochastic variables. The original paper establishes a strong global Markov property connecting transitional conditional independencies with suitable graphical separation criteria for directed mixed graphs with input nodes (iDMGs), together with a version of causal calculus for iDMGs in a general measure-theoretic setting. These notes aim to further illustrate the motivations behind this framework and its connections to the literature, highlight certain subtlies in the general measure-theoretic causal calculus, and extend the "one-line" formulation of the ID algorithm of Richardson et al. (Ann. Statist. 51(1):334--361, 2023) to the general measure-theoretic setting.

Parameterizing the approximate posterior of a generative model with neural networks has become a common theme in recent machine learning research. While providing appealing flexibility, this approach makes it difficult to impose or assess structural constraints such as conditional independence. We propose a framework for learning representations that relies on Auto-Encoding Variational Bayes and whose search space is constrained via kernel-based measures of independence. In particular, our method employs the $d$-variable Hilbert-Schmidt Independence Criterion (dHSIC) to enforce independence between the latent representations and arbitrary nuisance factors. We show how to apply this method to a range of problems, including the problems of learning invariant representations and the learning of interpretable representations. We also present a full-fledged application to single-cell RNA sequencing (scRNA-seq).

Causal discovery across multiple datasets is often constrained by data privacy regulations and cross-site heterogeneity, limiting the use of conventional methods that require a single, centralized dataset. To address these challenges, we introduce fedCI, a federated conditional independence test that rigorously handles heterogeneous datasets with non-identical sets of variables, site-specific effects, and mixed variable types, including continuous, ordinal, binary, and categorical variables. At its core, fedCI uses a federated Iteratively Reweighted Least Squares (IRLS) procedure to estimate the parameters of generalized linear models underlying likelihood-ratio tests for conditional independence. Building on this, we develop fedCI-IOD, a federated extension of the Integration of Overlapping Datasets (IOD) algorithm, that replaces its meta-analysis strategy and enables, for the fist time, federated causal discovery under latent confounding across distributed and heterogeneous datasets. By aggregating evidence federatively, fedCI-IOD not only preserves privacy but also substantially enhances statistical power, achieving performance comparable to fully pooled analyses and mitigating artifacts from low local sample sizes. Our tools are publicly available as the fedCI Python package, a privacy-preserving R implementation of IOD, and a web application for the fedCI-IOD pipeline, providing versatile, user-friendly solutions for federated conditional independence testing and causal discovery.

Lasso performance, with the added benefit of providing multiple solutions.

In order to overcome overestimation bias, ensemble methods for Q-learning have been investigated to exploit the diversity of multiple Q-functions. Since network initialization has been the predominant approach to promote diversity in Q-functions, heuristically designed diversity injection methods have been studied in the literature. However, previous studies have not attempted to approach guaranteed independence over an ensemble from a theoretical perspective.

These theoretical findings are supported by experimental results.