Tootfinder

Causal Decompositions of 1D Quantum Cellular Automata
Augustin Vanrietvelde, Octave Mestoudjian, Pablo Arrighi
https://arxiv.org/abs/2506.22219 https://

Causal Decompositions of 1D Quantum Cellular Automata
Understanding quantum theory's causal structure stands out as a major matter, since it radically departs from classical notions of causality. We present advances in the research program of causal decompositions, which investigates the existence of an equivalence between the causal and the compositional structures of unitary channels. Our results concern one-dimensional Quantum Cellular Automata (1D QCAs), i.e. unitary channels over a line of $N$ quantum systems (with or without periodic boundar…

Causal Inference for Aggregated Treatment
Carolina Caetano, Gregorio Caetano, Brantly Callaway, Derek Dyal
https://arxiv.org/abs/2506.22885 https://…

Causal Inference for Aggregated Treatment
In this paper, we study causal inference when the treatment variable is an aggregation of multiple sub-treatment variables. Researchers often report marginal causal effects for the aggregated treatment, implicitly assuming that the target parameter corresponds to a well-defined average of sub-treatment effects. We show that, even in an ideal scenario for causal inference such as random assignment, the weights underlying this average have some key undesirable properties: they are not unique, the…

Estimating Average Causal Effects with Incomplete Exposure and Confounders
Lan Wen, Glen McGee
https://arxiv.org/abs/2506.21786 https://

Estimating Average Causal Effects with Incomplete Exposure and Confounders
Standard methods for estimating average causal effects require complete observations of the exposure and confounders. In observational studies, however, missing data are ubiquitous. Motivated by a study on the effect of prescription opioids on mortality, we propose methods for estimating average causal effects when exposures and potential confounders may be missing. We consider missingness at random and additionally propose several specific missing not at random (MNAR) assumptions. Under our pr…

Some more constructions of $n-$cycle permutation polynomials
Varsha Jarali, Prasanna Poojary, Vadiraja Bhatta G. R
https://arxiv.org/abs/2506.21936 https:/…

Some more constructions of $n-$cycle permutation polynomials
$ n-$cycle permutation polynomials with small n have the advantage that their compositional inverses are efficient in terms of implementation. These permutation polynomials have significant applications in cryptography and coding theory. In this article, we propose criteria for the construction of $ n-$cycle permutation using linearized polynomial $ L(x) $ for larger $ n $. Furthermore, we investigate and generalize certain novel forms of $ n-$cycle permutation polynomials. Finally, we demonstr…

Estimating causal distances with non-causal ones
Beatrice Acciaio, Songyan Hou, Gudmund Pammer
https://arxiv.org/abs/2506.22421 https://

Estimating causal distances with non-causal ones
The adapted Wasserstein ($AW$) distance refines the classical Wasserstein ($W$) distance by incorporating the temporal structure of stochastic processes. This makes the $AW$-distance well-suited as a robust distance for many dynamic stochastic optimization problems where the classical $W$-distance fails. However, estimating the $AW$-distance is a notably challenging task, compared to the classical $W$-distance. In the present work, we build a sharp estimate for the $AW$-distance in terms of the…

Grow a Garden, a hit game on Roblox, has spawned a market where in-game items are sold for real money on sites like eBay, potentially violating Roblox's ToS (Cecilia D'Anastasio/Bloomberg)
https://www.bloomberg.com/news/articles/20…

Owning two Pixel phones running GrapheneOS made me both chuckle and tear up a little. It’s such an amazing operating system that I’m planning to start making monthly donations to support the project.
I had my Pixel 6a since early May 2023, but yesterday I upgraded to a Pixel 9a because the speakers and earpiece microphone on my 6a had started to fail badly, and the battery was showing its age.
I’m glad I made the upgrade, and now people can hear me clearly on the phone again.

Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements
Keith Barnatchez, Kevin P. Josey, Nima S. Hejazi, Bryan E. Shepherd, Giovanni Parmigiani, Rachel Nethery
https://arxiv.org/abs/2506.21777

Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements
Measurement error is a common challenge for causal inference studies using electronic health record (EHR) data, where clinical outcomes and treatments are frequently mismeasured. Researchers often address measurement error by conducting manual chart reviews to validate measurements in a subset of the full EHR data -- a form of two-phase sampling. To improve efficiency, phase-two samples are often collected in a biased manner dependent on the patients' initial, error-prone measurements. In this …

P-CRE-DML: A Novel Approach for Causal Inference in Non-Linear Panel Data
Amarendra Sharma
https://arxiv.org/abs/2506.23297 https://a…

P-CRE-DML: A Novel Approach for Causal Inference in Non-Linear Panel Data
This paper introduces a novel Proxy-Enhanced Correlated Random Effects Double Machine Learning (P-CRE-DML) framework to estimate causal effects in panel data with non-linearities and unobserved heterogeneity. Combining Double Machine Learning (DML, Chernozhukov et al., 2018), Correlated Random Effects (CRE, Mundlak, 1978), and lagged variables (Arellano & Bond, 1991) and innovating within the CRE-DML framework (Chernozhukov et al., 2022; Clarke & Polselli, 2025; Fuhr & Papies, 2024), we apply P…