Synthetic Interventions
- URL: http://arxiv.org/abs/2006.07691v6
- Date: Tue, 31 Oct 2023 16:39:22 GMT
- Title: Synthetic Interventions
- Authors: Anish Agarwal, Devavrat Shah, Dennis Shen
- Abstract summary: We learn the expected potential outcome associated with every intervention on every unit, totaling $N times D$ causal parameters.
We present a causal framework, synthetic interventions (SI), to infer these $N times D$ causal parameters.
We believe our results could have implications for the design of data-efficient randomized experiments.
- Score: 20.96904429337912
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Consider a setting with $N$ heterogeneous units (e.g., individuals,
sub-populations) and $D$ interventions (e.g., socio-economic policies). Our
goal is to learn the expected potential outcome associated with every
intervention on every unit, totaling $N \times D$ causal parameters. Towards
this, we present a causal framework, synthetic interventions (SI), to infer
these $N \times D$ causal parameters while only observing each of the $N$ units
under at most two interventions, independent of $D$. This can be significant as
the number of interventions, i.e., level of personalization, grows. Under a
novel tensor factor model across units, outcomes, and interventions, we prove
an identification result for each of these $N \times D$ causal parameters,
establish finite-sample consistency of our estimator along with asymptotic
normality under additional conditions. Importantly, our estimator also allows
for latent confounders that determine how interventions are assigned. The
estimator is further furnished with data-driven tests to examine its
suitability. Empirically, we validate our framework through a large-scale A/B
test performed on an e-commerce platform. We believe our results could have
implications for the design of data-efficient randomized experiments (e.g.,
randomized control trials) with heterogeneous units and multiple interventions.
Related papers
- Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making [58.06306331390586]
We introduce the notion of a margin complement, which measures how much a prediction score $S$ changes due to a thresholding operation.
We show that under suitable causal assumptions, the influences of $X$ on the prediction score $S$ are equal to the influences of $X$ on the true outcome $Y$.
arXiv Detail & Related papers (2024-05-24T11:22:19Z) - Collaborative non-parametric two-sample testing [55.98760097296213]
The goal is to identify nodes where the null hypothesis $p_v = q_v$ should be rejected.
We propose the non-parametric collaborative two-sample testing (CTST) framework that efficiently leverages the graph structure.
Our methodology integrates elements from f-divergence estimation, Kernel Methods, and Multitask Learning.
arXiv Detail & Related papers (2024-02-08T14:43:56Z) - Invariant Causal Prediction with Locally Linear Models [58.6243132840146]
We consider the task of identifying the causal parents of a target variable from observational data.
We introduce a practical method called LoLICaP, which is based on a hypothesis test for parent identification.
We show in a simplified setting that the statistical power of LoLICaP converges exponentially fast in the sample size.
arXiv Detail & Related papers (2024-01-10T15:34:42Z) - Nonparametric Identifiability of Causal Representations from Unknown
Interventions [63.1354734978244]
We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables.
Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
arXiv Detail & Related papers (2023-06-01T10:51:58Z) - Synthetic Combinations: A Causal Inference Framework for Combinatorial
Interventions [8.491098180590447]
We learn unit-specific potential outcomes for any combination of interventions, i.e., $N times 2p$ causal parameters.
Running $N times 2p$ experiments to estimate the various parameters is likely expensive and/or infeasible as $N$ and $p$ grow.
arXiv Detail & Related papers (2023-03-24T18:45:44Z) - A Robustness Analysis of Blind Source Separation [91.3755431537592]
Blind source separation (BSS) aims to recover an unobserved signal from its mixture $X=f(S)$ under the condition that the transformation $f$ is invertible but unknown.
We present a general framework for analysing such violations and quantifying their impact on the blind recovery of $S$ from $X$.
We show that a generic BSS-solution in response to general deviations from its defining structural assumptions can be profitably analysed in the form of explicit continuity guarantees.
arXiv Detail & Related papers (2023-03-17T16:30:51Z) - On counterfactual inference with unobserved confounding [36.18241676876348]
Given an observational study with $n$ independent but heterogeneous units, our goal is to learn the counterfactual distribution for each unit.
We introduce a convex objective that pools all $n$ samples to jointly learn all $n$ parameter vectors.
We derive sufficient conditions for compactly supported distributions to satisfy the logarithmic Sobolev inequality.
arXiv Detail & Related papers (2022-11-14T04:14:37Z) - On the Identifiability and Estimation of Causal Location-Scale Noise
Models [122.65417012597754]
We study the class of location-scale or heteroscedastic noise models (LSNMs)
We show the causal direction is identifiable up to some pathological cases.
We propose two estimators for LSNMs: an estimator based on (non-linear) feature maps, and one based on neural networks.
arXiv Detail & Related papers (2022-10-13T17:18:59Z) - Revealing Unobservables by Deep Learning: Generative Element Extraction
Networks (GEEN) [5.3028918247347585]
This paper proposes a novel method for estimating realizations of a latent variable $X*$ in a random sample.
To the best of our knowledge, this paper is the first to provide such identification in observation.
arXiv Detail & Related papers (2022-10-04T01:09:05Z) - Metric Entropy Duality and the Sample Complexity of Outcome
Indistinguishability [7.727052811126007]
In outcome indistinguishability, the goal is to output a predictor that cannot be distinguished from the target predictor.
We show that the sample complexity of outcome indistinguishability is characterized by the metric entropy of $P$ w.r.t.
This equivalence makes an intriguing connection to the long-standing metric entropy duality conjecture in convex geometry.
arXiv Detail & Related papers (2022-03-09T06:02:31Z) - Efficient Intervention Design for Causal Discovery with Latents [30.721629140295178]
We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process.
We consider two intervention cost models: (1) a linear cost model where the cost of an intervention on a subset of variables has a linear form, and (2) an identity cost model where the cost of an intervention is the same, regardless of what variables it is on.
arXiv Detail & Related papers (2020-05-24T12:53:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.