Developing Counterfactual Inference Methods for Clinical Trial Recruitment and Effective Integration of Weak Instrumental Variables.

该项目属于以下EPSRC研究领域：医疗保健技术、统计学和应用概率、数字孪生计算统计学和机器学习领域的一个当前研究问题是，算法是否可以自动推断出系统的行为和模式，这些行为和模式在其潜在机制中表现出内在的因果关系。人类通常擅长识别自然界中的这种关系（例如，我们知道太阳升起导致温度上升，而不是相反）。然而，机器通常很难直接学习这些关系，一个潜在的研究途径是开发能够自动识别数据变量之间潜在因果关系的方法。已经开发了许多方法（称为因果结构学习算法）来自动学习这些关系，并且在小数据集/特征和高斯数据中表现良好。然而，它们很难很好地扩展到更大、更复杂的数据集，这些数据集不是线性高斯的。我博士期间的一个研究途径是通过开发可扩展的结构学习方法来解决这个问题，这种方法可以处理大型数据集，而不会牺牲推断关系的保真度。即使描述一组变量的因果路径是已知的，在模型中准确推断定量关系也是具有挑战性的。这些问题因混杂因素的存在而加剧，特别是在未被观察到（甚至是未知的）情况下。在医学临床医生的背景下，这些可能是一些变量，如性别或年龄，这不仅会影响医生指定的治疗方案，还会影响治疗本身的效果。考虑这些变量的黄金标准是通过运行随机对照试验（rct），在人为选择的亚群体中手动干预和控制这些差异。然而，通常情况下，随机对照试验是不道德的（例如，已知的“治疗”是有害的）或过于昂贵。此外，可能有大量可免费获得的观察数据（即不是从随机对照试验获得的），可以用来代替进行随机试验。尽管观测数据集的规模很大，但它们的完整性和完整性可能存在问题。例如，可能存在未被记录的变量（即潜在的混杂因素）。这些变量可能是我们已知的（专家可能能够对数据中存在和不存在的特征之间的关系进行编码），但也可能是未知的。这个问题可能会激发另一个研究方向，即寻找其他方法来捕捉这些混杂因素的行为，并减少模型推断的偏差。另一个阻碍采用因果方法的关键障碍是它们能够扩展到大型数据/特性集。对于更大、更复杂的数据集，当前方法的运行时间通常难以扩展。另一个潜在的研究途径是探索不同类型的近似，这些近似可以在不牺牲太多预测能力的情况下更有效地扩展。虽然上面讨论了推动因果方法的方法学角度，但我对将这些方法应用于生物/医疗保健问题有浓厚的兴趣。特别是，如何将临床患者的深度生成模型与结构化因果模型相结合，以创建患者的因果数字双胞胎，而不是从混杂生成模型中提取有偏差的样本。

This project falls within the following EPSRC research areas:Healthcare TechnologiesStatistics and Applied ProbabilityDigital TwinsA current research question in the Computational Statistics and ML spaces is whether algorithms can automatically infer the behaviours and patterns of systems which demonstrate inherent causal relationships in their underlying mechanisms. Humans often excel at identifying such relationships in nature (e.g. we know the rising sun causes temperatures to rise rather than the other way round). Machines however, generally struggle to learn these relationships directly, and one potential research avenue is to develop methods which can automatically identify potential causal relationships between data variables. A number of methods (known as Causal Structure Learning algorithms) have been developed to learn these relationships automatically, and perform well with with small datasets/features and Gaussian data. However, they struggle to scale well with larger, complex datasets which are not linearly Gaussian. An avenue for research during my DPhil is to address this problem through developing scalable Structure Learning methods which can process large datasets without sacrificing the fidelity of the inferred relationships.Even if the causal pathways describing a set of variables is known, accurately inferring quantitative relationships in a model can be challenging. These problems are exacerbated by the presence of confounders, particularly if unobserved (or even if unknown). In the context of medical clinicians, these could be some variable like sex or age which not only impacts the treatment assigned by a doctor, but also the effect of the treatment itself. The gold standard for accounting for these variables is by running Randomised Controlled Trials (RCTs) to manually intervene and control for these discrepancies across an artificially selected subpopulation. However, there are often cases where RCTs are unethical (e.g. with "treatments" that are known to be harmful) or too expensive. Additionally, there may be a wealth of freely available observational data (i.e. not obtained from an RCT) which could be used in lieu of running a randomised trial.Despite the size of observational datasets, there may be issues with their completeness and integrity. For example, there may be variables (i.e. potential confounders) which were simply not recorded. Thesevariables might be known to us (where experts may be able to encode relationships between present and absent features in our data), but may also be unknown. This problem may motivate another research direction which looks at alternative approaches to capture the behaviour of these confounders and reduce the biases inferred by the model.Another key roadblock a ecting the adoption of causal methods is their ability to scale to large data/feature sets. The run time of current methods often scale extremely poorly to larger, more complex, datasets. Another avenue for potential research would be to explore di erent types of approximations which would scale more e ciently without sacri cing much predictive power.Whilst the above discuss the methodological angles for pushing the needle forward for causal methods, I have a strong interest in applying these methods to biological/healthcare problems. In particular, how can Deep Generative models of clinical patients be combined with structured causal models to create causal Digital Twins of patients rather than biased samples drawn from confounded generative models.