24,551 research outputs found
MALTS: Matching After Learning to Stretch
We introduce a flexible framework that produces high-quality almost-exact
matches for causal inference. Most prior work in matching uses ad-hoc distance
metrics, often leading to poor quality matches, particularly when there are
irrelevant covariates. In this work, we learn an interpretable distance metric
for matching, which leads to substantially higher quality matches. The learned
distance metric stretches the covariate space according to each covariate's
contribution to outcome prediction: this stretching means that mismatches on
important covariates carry a larger penalty than mismatches on irrelevant
covariates. Our ability to learn flexible distance metrics leads to matches
that are interpretable and useful for the estimation of conditional average
treatment effects.Comment: 40 pages, 5 Tables, 12 Figure
A Data-Aided Channel Estimation Scheme for Decoupled Systems in Heterogeneous Networks
Uplink/downlink (UL/DL) decoupling promises more flexible cell association
and higher throughput in heterogeneous networks (HetNets), however, it hampers
the acquisition of DL channel state information (CSI) in time-division-duplex
(TDD) systems due to different base stations (BSs) connected in UL/DL. In this
paper, we propose a novel data-aided (DA) channel estimation scheme to address
this problem by utilizing decoded UL data to exploit CSI from received UL data
signal in decoupled HetNets where a massive multiple-input multiple-output BS
and dense small cell BSs are deployed. We analytically estimate BER performance
of UL decoded data, which are used to derive an approximated normalized mean
square error (NMSE) expression of the DA minimum mean square error (MMSE)
estimator. Compared with the conventional least square (LS) and MMSE, it is
shown that NMSE performances of all estimators are determined by their
signal-to-noise ratio (SNR)-like terms and there is an increment consisting of
UL data power, UL data length and BER values in the SNR-like term of DA method,
which suggests DA method outperforms the conventional ones in any scenarios.
Higher UL data power, longer UL data length and better BER performance lead to
more accurate estimated channels with DA method. Numerical results verify that
the analytical BER and NMSE results are close to the simulated ones and a
remarkable gain in both NMSE and DL rate can be achieved by DA method in
multiple scenarios with different modulations
Causal Inference under Network Interference Using a Mixture of Randomized Experiments
In randomized experiments, the classic stable unit treatment value assumption
(SUTVA) states that the outcome for one experimental unit does not depend on
the treatment assigned to other units. However, the SUTVA assumption is often
violated in applications such as online marketplaces and social networks where
units interfere with each other. We consider the estimation of the average
treatment effect in a network interference model using a mixed randomization
design that combines two commonly used experimental methods: Bernoulli
randomized design, where treatment is independently assigned for each
individual unit, and cluster-based design, where treatment is assigned at an
aggregate level. Essentially, a mixed randomization experiment runs these two
designs simultaneously, allowing it to better measure the effect of network
interference. We propose an unbiased estimator for the average treatment effect
under the mixed design and show the variance of the estimator is bounded by
where is the maximum degree of the network, is
the network size, and is the probability of treatment. We also establish a
lower bound of for the variance of any mixed
design. For a family of sparse networks characterized by a growth constant
, we improve the upper bound to .
Furthermore, when interference weights on the edges of the network are unknown,
we propose a weight-invariant design that achieves a variance bound of
Minimizing Interference and Selection Bias in Network Experiment Design
Current approaches to A/B testing in networks focus on limiting interference,
the concern that treatment effects can "spill over" from treatment nodes to
control nodes and lead to biased causal effect estimation. Prominent methods
for network experiment design rely on two-stage randomization, in which
sparsely-connected clusters are identified and cluster randomization dictates
the node assignment to treatment and control. Here, we show that cluster
randomization does not ensure sufficient node randomization and it can lead to
selection bias in which treatment and control nodes represent different
populations of users. To address this problem, we propose a principled
framework for network experiment design which jointly minimizes interference
and selection bias. We introduce the concepts of edge spillover probability and
cluster matching and demonstrate their importance for designing network A/B
testing. Our experiments on a number of real-world datasets show that our
proposed framework leads to significantly lower error in causal effect
estimation than existing solutions.Comment: This paper has been accepted at the International AAAI Conference on
Web and Social Media (ICWSM 2020
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