In many real-world scenarios, such as gene knockout experiments, targeted
interventions are often accompanied by unknown interventions at off-target
sites. Moreover, different units can get randomly exposed to different unknown
interventions, thereby creating a mixture of interventions. Identifying
different components of this mixture can be very valuable in some applications.
Motivated by such situations, in this work, we study the problem of identifying
all components present in a mixture of interventions on a given causal Bayesian
Network. We construct an example to show that, in general, the components are
not identifiable from the mixture distribution. Next, assuming that the given
network satisfies a positivity condition, we show that, if the set of mixture
components satisfy a mild exclusion assumption, then they can be uniquely
identified. Our proof gives an efficient algorithm to recover these targets
from the exponentially large search space of possible targets. In the more
realistic scenario, where distributions are given via finitely many samples, we
conduct a simulation study to analyze the performance of an algorithm derived
from our identifiability proof.

研究了在引起贝叶斯网络中识别所有干预混合组分的问题，证明了在满足积极性条件的情况下，如果混合组分满足接受排除假设，则可以唯一地识别它们。

分离未知因果干预混合物

Disentangling Mixtures of Unknown Causal Interventions

Instrumental variable methods provide a powerful approach to estimating
causal effects in the presence of unobserved confounding. But a key challenge
when applying them is the reliance on untestable "exclusion" assumptions that
rule out any relationship between the instrument variable and the response that
is not mediated by the treatment. In this paper, we show how to perform
consistent IV estimation despite violations of the exclusion assumption. In
particular, we show that when one has multiple candidate instruments, only a
majority of these candidates---or, more generally, the modal candidate-response
relationship---needs to be valid to estimate the causal effect. Our approach
uses an estimate of the modal prediction from an ensemble of instrumental
variable estimators. The technique is simple to apply and is "black-box" in the
sense that it may be used with any instrumental variable estimator as long as
the treatment effect is identified for each valid instrument independently. As
such, it is compatible with recent machine-learning based estimators that allow
for the estimation of conditional average treatment effects (CATE) on complex,
high dimensional data. Experimentally, we achieve accurate estimates of
conditional average treatment effects using an ensemble of deep network-based
estimators, including on a challenging simulated Mendelian Randomization
problem.

本文提出一种方法，使用多个工具变量估计器计算最可能的因果效应，从而在不依赖于「排除假设」的情况下进行一致的工具变量估计。