Scientific hypotheses typically concern specific aspects of complex,
imperfectly understood or entirely unknown mechanisms, such as the effect of
gene expression levels on phenotypes or how microbial communities influence
environmental health. Such queries are inherently causal (rather than purely
associational), but in many settings, experiments can not be conducted directly
on the target variables of interest, but are indirect. Therefore, they perturb
the target variable, but do not remove potential confounding factors. If,
additionally, the resulting experimental measurements are multi-dimensional and
the studied mechanisms nonlinear, the query of interest is generally not
identified. We develop an adaptive strategy to design indirect experiments that
optimally inform a targeted query about the ground truth mechanism in terms of
sequentially narrowing the gap between an upper and lower bound on the query.
While the general formulation consists of a bi-level optimization procedure, we
derive an efficiently estimable analytical kernel-based estimator of the bounds
for the causal effect, a query of key interest, and demonstrate the efficacy of
our approach in confounded, multivariate, nonlinear synthetic settings.

通过二级优化程序，我们开发了一种自适应策略，设计间接实验来在选定的目标上尽可能减小询问的上下界之间的差距，从而告知关于真实机制的查询。

目标定向顺序间接实验设计

Targeted Sequential Indirect Experiment Design

Large language models (LLMs) have transformed AI and achieved breakthrough
performance on a wide range of tasks that require human intelligence. In
science, perhaps the most interesting application of LLMs is for hypothesis
formation. A feature of LLMs, which results from their probabilistic structure,
is that the output text is not necessarily a valid inference from the training
text. These are 'hallucinations', and are a serious problem in many
applications. However, in science, hallucinations may be useful: they are novel
hypotheses whose validity may be tested by laboratory experiments. Here we
experimentally test the use of LLMs as a source of scientific hypotheses using
the domain of breast cancer treatment. We applied the LLM GPT4 to hypothesize
novel pairs of FDA-approved non-cancer drugs that target the MCF7 breast cancer
cell line relative to the non-tumorigenic breast cell line MCF10A. In the first
round of laboratory experiments GPT4 succeeded in discovering three drug
combinations (out of 12 tested) with synergy scores above the positive
controls. These combinations were itraconazole + atenolol, disulfiram +
simvastatin and dipyridamole + mebendazole. GPT4 was then asked to generate new
combinations after considering its initial results. It then discovered three
more combinations with positive synergy scores (out of four tested), these were
disulfiram + fulvestrant, mebendazole + quinacrine and disulfiram + quinacrine.
A limitation of GPT4 as a generator of hypotheses was that its explanations for
them were formulaic and unconvincing. We conclude that LLMs are an exciting
novel source of scientific hypotheses.

通过实验测试，我们发现大型语言模型是一种令人兴奋的新型科学假设来源，并成功应用于乳腺癌治疗领域来生成新的、与非肿瘤性乳腺细胞系 MCF10A 相比针对 MCF7 乳腺癌细胞系的 FDA 批准的非癌症药物组合，并发现多个具有积极协同效应的药物组合。

大型语言模型进行科学假设生成的实验室验证：在乳腺癌治疗中的应用

Scientific Hypothesis Generation by a Large Language Model: Laboratory  Validation in Breast Cancer Treatment

The enhanced performance of AI has accelerated its integration into
scientific research. In particular, the use of generative AI to create
scientific hypotheses is promising and is increasingly being applied across
various fields. However, when employing AI-generated hypotheses for critical
decisions, such as medical diagnoses, verifying their reliability is crucial.
In this study, we consider a medical diagnostic task using generated images by
diffusion models, and propose a statistical test to quantify its reliability.
The basic idea behind the proposed statistical test is to employ a selective
inference framework, where we consider a statistical test conditional on the
fact that the generated images are produced by a trained diffusion model. Using
the proposed method, the statistical reliability of medical image diagnostic
results can be quantified in the form of a p-value, allowing for
decision-making with a controlled error rate. We show the theoretical validity
of the proposed statistical test and its effectiveness through numerical
experiments on synthetic and brain image datasets.

通过使用生成 AI 创造科学假设，我们提出了一种统计测试方法来验证医学图像诊断结果的可靠性，通过控制错误率，以 p 值的形式量化医学图像诊断结果的统计可靠性。

扩散模型生成假设的统计检验

Statistical Test for Generated Hypotheses by Diffusion Models

Counterfactual analysis is intuitively performed by humans on a daily basis
eg. "What should I have done differently to get the loan approved?". Such
counterfactual questions also steer the formulation of scientific hypotheses.
More formally it provides insights about potential improvements of a system by
inferring the effects of hypothetical interventions into a past observation of
the system's behaviour which plays a prominent role in a variety of industrial
applications. Due to the hypothetical nature of such analysis, counterfactual
distributions are inherently ambiguous. This ambiguity is particularly
challenging in continuous settings in which a continuum of explanations exist
for the same observation. In this paper, we address this problem by following a
hierarchical Bayesian approach which explicitly models such uncertainty. In
particular, we derive counterfactual distributions for a Bayesian Warped
Gaussian Process thereby allowing for non-Gaussian distributions and
non-additive noise. We illustrate the properties our approach on a synthetic
and on a semi-synthetic example and show its performance when used within an
algorithmic recourse downstream task.

通过采用贝叶斯层级模型来建模不确定性，本文解决了连续设置中对反事实分布的模棱两可性问题，特别是在贝叶斯变形高斯过程中，允许非高斯分布和非加法噪声，成功应用于一个合成和半合成的案例，并展示了在算法补救下游任务中的性能。