The Causal Continuum - Transforming Modelling and Computation in Causal Inference

因果连续体 - 转变因果推理中的建模和计算

• 批准号: EP/W024330/1
• 负责人: Ricardo Silva
• 金额: $ 171.2万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW024330%2F1
• 关键词: Causal; Continuum; Transforming; Modelling; Computation

A central aspect of science and engineering is to be able to answer "what if" questions. What will happen if this gene suffers a mutation? What are the public health consequences of having this social benefit cut? What can we do to mitigate disparities among social groups? To which extent are lockdowns useful to mitigate a pandemic? Which ramifications will take place if failures occur at these points of a major logistical operation such as food supply chains?These are cause-effect questions. Answering them is hard because it involves change. Historical data may fail to capture the implications of change, placing causal questions out of the comfort zone by which data is used to inform decisions. It is one thing to predict the life expectancy of a smoker, as done by public health officials or insurance companies. It is much harder to understand what will happen if we convince someone to stop smoking, as historical data may have a substantive number of cases where people stopped smoking shortly before dying of respiratory disease, due to discomfort. A statistical or machine learning method oblivious to these causal explanations may actually say that stopping smoking is bad for one's health.Ideally, we would like to perform randomised controlled trials where the choice of action to be taken is decided by the flip of coin, so that confounding factors between cause and effect are overridden. This removal of confounding is necessary to show convincingly, for instance, that a covid-19 vaccination works due to biological processes as opposed to sociological confounding factors among those who choose to be vaccinated and their health outcomes. However, in many cases such trials can be very expensive (understanding genetic networks involves a large experimental space) or unethical (we cannot force someone to smoke or not), and even when they take place, a controlled trial may not fully control the factor of interest (we can randomly assign a drug or placebo to a patient, but we may not have the means to make the patient comply with the treatment if they stay at home).Data scientists have not ignored these problems, and we can thank the hard work of epidemiologists, for instance, for presenting a convincing case establishing the harmful link between smoking and lung cancer. But without randomised trials, the answer to a "what if" question requires assumptions or otherwise it is unknowable. This means that causal inference progresses slowly and is prone to mistakes. Part of the reason is that, traditionally, methods for causal inference largely rely on pre-defined families of assumptions chosen by statisticians designing methods that will provide unambiguous answers. Applied scientists then choose to adopt a particular method according to what manages to be a good enough approximation to their understanding of the world (one simple case: assume we have no common causes that are not measured in the data!). Although there are tools for sensitivity analysis (what if assumptions are violated in some particular ways?), they don't address the main issue directly: a domain-expert should be given the chance of specifying upfront assumptions according to the way they see appropriate, and not be artificially told a single, convenient answer, but what indeed can be disentangled from the observational data given the information provided. One of the reasons this workflow is not popular is the need for computationally-intensive algorithms to deduce the consequences of such assumptions. This project has the ambition of changing the common practice for causal inference, increasing transparency and the speed by which we understand the limits of our knowledge and where to look for in order to progress. It will rely on cutting-edge algorithms for providing a flexible sandbox for domain experts to express their knowledge on a very flexible way, while offering also the backend support for the sophisticated computational methods needed.

科学和工程的一个核心方面是能够回答“如果”的问题。如果这个基因发生突变会发生什么？削减这项社会福利会对公共卫生造成什么后果？我们可以做些什么来减少社会群体之间的差距？封锁在多大程度上有助于缓解大流行？如果在食品供应链等重大物流运作的这些环节发生故障，将产生哪些后果？这些是因果问题。改变它们是困难的，因为它涉及到改变。历史数据可能无法捕捉变化的影响，将因果问题置于数据用于决策的舒适区之外。预测吸烟者的预期寿命是一回事，就像公共卫生官员或保险公司所做的那样。很难理解如果我们说服某人戒烟会发生什么，因为历史数据可能有大量的案例，人们在死于呼吸系统疾病之前不久就停止了吸烟，由于不适。一种统计学或机器学习方法无视这些因果解释，实际上可能会说戒烟对健康有害。理想情况下，我们希望进行随机对照试验，通过抛硬币决定采取何种行动，从而忽略因果之间的混淆因素。这种消除混杂因素的做法对于令人信服地证明以下事实是必要的：例如，新型冠状病毒疫苗接种的有效性是由于生物学过程，而不是选择接种疫苗的人及其健康结果中的社会学混杂因素。然而，在许多情况下，（了解基因网络涉及很大的实验空间）或不道德（我们不能强迫某人吸烟或不吸烟），即使当它们发生时，对照试验也可能无法完全控制利益因素（我们可以随机给一个病人分配一种药物或安慰剂，但如果病人呆在家里，我们可能没有办法让他们遵守治疗）。数据科学家并没有忽视这些问题，我们可以感谢流行病学家的辛勤工作，例如，他提出了一个令人信服的案例，证明了吸烟与肺癌之间的有害联系。但是，如果没有随机试验，“如果”问题的答案需要假设，否则它是不可知的。这意味着因果推理进展缓慢，容易出错。部分原因是，传统上，因果推理的方法在很大程度上依赖于统计学家选择的预先定义的假设家族，这些假设家族设计的方法将提供明确的答案。然后，应用科学家根据他们对世界的理解，选择采用一种特定的方法（一个简单的例子：假设我们没有数据中没有测量的共同原因！）。虽然有敏感性分析的工具（如果假设以某些特定的方式被违反了呢？），他们没有直接解决主要问题：领域专家应该有机会根据他们认为合适的方式指定预先假设，而不是人为地告诉一个单一的，方便的答案，而是在提供信息的情况下确实可以从观测数据中解脱出来。这种工作流程不受欢迎的原因之一是需要计算密集型算法来推断这种假设的后果。该项目的目标是改变因果推理的常见做法，提高透明度和速度，让我们了解我们知识的局限性，以及在哪里寻找进步。它将依赖于尖端的算法，为领域专家提供一个灵活的沙箱，以非常灵活的方式表达他们的知识，同时还为所需的复杂计算方法提供后端支持。

项目成果

Seconder of the vote of thanks to Evans & Didelez and contribution to the Discussion of 'Parameterizing and Simulating from Causal Models'

对埃文斯表示感谢的附议票

• DOI: 10.1093/jrsssb/qkae013
• 发表时间: 2024
• 期刊: Statistical Methodology
• 作者: Silva R

Stochastic Causal Programming for Bounding Treatment Effects

用于限制治疗效果的随机因果规划

• 发表时间: 2023
• 作者: K. Padh

Intervention Generalization: A View from Factor Graph Models

干预泛化：因子图模型的视角

• DOI: 10.48550/arxiv.2306.04027
• 发表时间: 2023
• 作者: Bravo-Hermsdorff G