An important problem across disciplines is the discovery of interventions that produce a desired outcome. When the space of possible interventions is large, making an exhaustive search infeasible, experimental design strategies are needed. In this context, encoding the causal relationships between the variables, and thus the effect of interventions on the system, is critical in order to identify desirable interventions efficiently. We develop an iterative causal method to identify optimal interventions, as measured by the discrepancy between the post-interventional mean of the distribution and a desired target mean. We formulate an active learning strategy that uses the samples obtained so far from different interventions to update the belief about the underlying causal model, as well as to identify samples that are most informative about optimal interventions and thus should be acquired in the next batch. The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for efficient optimization. The resulting algorithms are theoretically grounded with information-theoretic bounds and provable consistency results. We illustrate the method on both synthetic data and real-world biological data, namely gene expression data from Perturb-CITE-seq experiments, to identify optimal perturbations that induce a specific cell state transition; the proposed causal approach is observed to achieve better sample efficiency compared to several baselines. In both cases we observe that the causally informed acquisition function notably outperforms existing criteria allowing for optimal intervention design with significantly less experiments.
翻译:学科间的一个重要问题是发现能产生理想结果的干预措施。当可能采取的干预措施的空间很大,使得彻底的搜索不可行时,需要实验性的设计战略。在这方面,将变量之间的因果关系以及干预对系统的影响进行编码至关重要,以便有效确定适当的干预措施。我们开发了一种迭代因果方法,以确定最佳干预措施,以干预后分布平均值和预期目标平均值之间的差异来衡量。我们制定了积极的学习战略,利用从不同干预措施获得的样本,更新对基本因果模型的信念,并查明对最佳干预措施最为知情的样本,从而在下一批产品中应当获得。该方法对因果模型的因果关系进行编码更新,并使用精心设计、因果知情的购置功能确定干预措施的优先次序。这一获取功能以封闭的形式进行评估,以便进行高效优化。由此得出的算法以信息-理论界限和最佳干预结果为理论依据。我们用合成数据和真实世界生物数据的方法,即 Perturb-CITE的基因表达数据,从而在下一个批次批次中获取。该方法对因果性模型进行了精确性更新,从而将某些特定因果基质标准进行我们所观察到的最佳标准。