8,631 research outputs found
Generalization bound for estimating causal effects from observational network data
Estimating causal effects from observational network data is a significant
but challenging problem. Existing works in causal inference for observational
network data lack an analysis of the generalization bound, which can
theoretically provide support for alleviating the complex confounding bias and
practically guide the design of learning objectives in a principled manner. To
fill this gap, we derive a generalization bound for causal effect estimation in
network scenarios by exploiting 1) the reweighting schema based on joint
propensity score and 2) the representation learning schema based on Integral
Probability Metric (IPM). We provide two perspectives on the generalization
bound in terms of reweighting and representation learning, respectively.
Motivated by the analysis of the bound, we propose a weighting regression
method based on the joint propensity score augmented with representation
learning. Extensive experimental studies on two real-world networks with
semi-synthetic data demonstrate the effectiveness of our algorithm
Estimation of individual causal effects in network setup for multiple treatments
We study the problem of estimation of Individual Treatment Effects (ITE) in
the context of multiple treatments and networked observational data. Leveraging
the network information, we aim to utilize hidden confounders that may not be
directly accessible in the observed data, thereby enhancing the practical
applicability of the strong ignorability assumption. To achieve this, we first
employ Graph Convolutional Networks (GCN) to learn a shared representation of
the confounders. Then, our approach utilizes separate neural networks to infer
potential outcomes for each treatment. We design a loss function as a weighted
combination of two components: representation loss and Mean Squared Error (MSE)
loss on the factual outcomes. To measure the representation loss, we extend
existing metrics such as Wasserstein and Maximum Mean Discrepancy (MMD) from
the binary treatment setting to the multiple treatments scenario. To validate
the effectiveness of our proposed methodology, we conduct a series of
experiments on the benchmark datasets such as BlogCatalog and Flickr. The
experimental results consistently demonstrate the superior performance of our
models when compared to baseline methods.Comment: 7 pages, accepted at AAAI-GCLR 202
Inferring Causal Effects Under Heterogeneous Peer Influence
Causal inference in networks should account for interference, which occurs
when a unit's outcome is influenced by treatments or outcomes of peers. There
can be heterogeneous peer influence between units when a unit's outcome is
subjected to variable influence from different peers based on their attributes
and relationships, or when each unit has a different susceptibility to peer
influence. Existing solutions to causal inference under interference consider
either homogeneous influence from peers or specific heterogeneous influence
mechanisms (e.g., based on local neighborhood structure). This paper presents a
methodology for estimating individual causal effects in the presence of
heterogeneous peer influence due to arbitrary mechanisms. We propose a
structural causal model for networks that can capture arbitrary assumptions
about network structure, interference conditions, and causal dependence. We
identify potential heterogeneous contexts using the causal model and propose a
novel graph neural network-based estimator to estimate individual causal
effects. We show that existing state-of-the-art methods for individual causal
effect estimation produce biased results in the presence of heterogeneous peer
influence, and that our proposed estimator is robust
Regression adjustments for estimating the global treatment effect in experiments with interference
Standard estimators of the global average treatment effect can be biased in
the presence of interference. This paper proposes regression adjustment
estimators for removing bias due to interference in Bernoulli randomized
experiments. We use a fitted model to predict the counterfactual outcomes of
global control and global treatment. Our work differs from standard regression
adjustments in that the adjustment variables are constructed from functions of
the treatment assignment vector, and that we allow the researcher to use a
collection of any functions correlated with the response, turning the problem
of detecting interference into a feature engineering problem. We characterize
the distribution of the proposed estimator in a linear model setting and
connect the results to the standard theory of regression adjustments under
SUTVA. We then propose an estimator that allows for flexible machine learning
estimators to be used for fitting a nonlinear interference functional form. We
propose conducting statistical inference via bootstrap and resampling methods,
which allow us to sidestep the complicated dependences implied by interference
and instead rely on empirical covariance structures. Such variance estimation
relies on an exogeneity assumption akin to the standard unconfoundedness
assumption invoked in observational studies. In simulation experiments, our
methods are better at debiasing estimates than existing inverse propensity
weighted estimators based on neighborhood exposure modeling. We use our method
to reanalyze an experiment concerning weather insurance adoption conducted on a
collection of villages in rural China.Comment: 38 pages, 7 figure
Deep Learning of Potential Outcomes
This review systematizes the emerging literature for causal inference using
deep neural networks under the potential outcomes framework. It provides an
intuitive introduction on how deep learning can be used to estimate/predict
heterogeneous treatment effects and extend causal inference to settings where
confounding is non-linear, time varying, or encoded in text, networks, and
images. To maximize accessibility, we also introduce prerequisite concepts from
causal inference and deep learning. The survey differs from other treatments of
deep learning and causal inference in its sharp focus on observational causal
estimation, its extended exposition of key algorithms, and its detailed
tutorials for implementing, training, and selecting among deep estimators in
Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Causal-Inference
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