This problem is equivalent to solving a Fredholm integral equation of the first kind. This is, solving for $p(x)$ such that:

$$
p(y) = \int_{\text{supp}(X)} p(y\mid x)\, p(x)\, \text{d}x,\quad \forall y\in \text{supp}(Y)
$$ 


In general, this is an ill-posed inverse problem, and thus it is challenging for both analytical and numerical approaches. As @Glen_b mentions, a characterization of the support of $X$ is needed, as some approaches start off with its discretization. 

For instance, notice that:

$$
p(x) = \int p(x\mid y)\,p(y)\,\text{d}y = \int \frac{p(y\mid x)\, p(x)}{\int p(y\mid \tilde{x})\, p(\tilde{x})\,\text{d}\tilde{x}}\,p(y)\,\text{d}y
$$

This motivates a simple fixed-point iteration to solve for $p(x)$ in the case of $X$ being discrete with support $\{c_1,\cdots, c_d\}$, $d\geq 2$, and $p(y\mid x)$ being available for direct evaluation:

1. Initialize $p_0(x)=1/d$, $\forall x\in\{c_1,\cdots, c_d\}$ 
2. For each iteration step $n$, draw $m$ i.i.d. samples from $p(y)$, and let:

$$
p_{n}(x) = \frac{1}{m}\sum_{j=1}^m \frac{p(y_i\mid x)\, p_{n-1}(x)}{\sum_{i=1}^d p(y_j\mid c_i)\, p_{n-1}(c_i)},\quad \forall x\in\{c_1,\cdots, c_d\}
$$

The motivation is that, for a sufficiently large $N$, $p_N(x)\approx p(x)$ in its support. This is based on: 


> Kondor (1983). _Method of convergent weights — An iterative procedure for solving Fredholm's integral equations of the first kind_. Nuclear Instruments and Methods in Physics Research, Volume 216, Issues 1–2, 1983, Pages 177-181, ISSN 0167-5087, https://doi.org/10.1016/0167-5087(83)90348-4.

There are more refined approaches including:
- **Expectation Maximization Smoothing (EMS)**: Silverman et al (1990). _A Smoothed EM Approach to Indirect Estimation Problems, with Particular, Reference to Stereology and Emission Tomography_. Journal of the Royal Statistical Society. Series B (Methodological), 52(2), 271–324. http://www.jstor.org/stable/2345438
- **Iterative Bayes (IB)**: Ma (2011). _Indirect density estimation using the iterative Bayes algorithm_.
Computational Statistics & Data Analysis, Volume 55, Issue 3, 2011,
Pages 1180-1195, ISSN 0167 9473, https://doi.org/10.1016/j.csda.2010.09.018.
- **Sequential Monte Carlo (SMC)**: Crucinio et al (2023). _A Particle Method for Solving Fredholm Equations of the First Kind_. Journal of the American Statistical Association, 118(542), 937–947. https://doi.org/10.1080/01621459.2021.1962328